1 00:00:00,000 --> 00:00:02,405 [NO AUDIO] 2 00:00:08,865 --> 00:00:10,490 CATHY L. DRENNAN: So welcome everybody. 3 00:00:10,490 --> 00:00:13,450 I hope you're really excited about this semester coming 4 00:00:13,450 --> 00:00:16,360 up, and getting involved with teaching. 5 00:00:16,360 --> 00:00:19,600 So I am Professor Cathy Drennan, a professor 6 00:00:19,600 --> 00:00:23,740 here at MIT in the Chemistry and Biology Department. 7 00:00:23,740 --> 00:00:26,290 And I want to start today-- 8 00:00:26,290 --> 00:00:30,570 so this workshop is about stereotype, stereotype threat, 9 00:00:30,570 --> 00:00:33,740 wise criticism, unconscious bias. 10 00:00:33,740 --> 00:00:37,540 And I want to start, because you're going to be teachers, 11 00:00:37,540 --> 00:00:39,040 with telling you about my motivation 12 00:00:39,040 --> 00:00:41,140 for doing this training. 13 00:00:41,140 --> 00:00:43,000 I think she's a chemist. 14 00:00:43,000 --> 00:00:45,880 Why is she standing up here telling us about this? 15 00:00:45,880 --> 00:00:49,060 So as teachers, it's good to tell your students 16 00:00:49,060 --> 00:00:50,720 about your motivation. 17 00:00:50,720 --> 00:00:52,780 So what is my motivation? 18 00:00:52,780 --> 00:00:57,220 Well, my motivation was-- this a story from a number of years 19 00:00:57,220 --> 00:01:02,530 ago, and I was asked to attend a workshop happening 20 00:01:02,530 --> 00:01:05,770 at Harvard on increasing diversity in the sciences. 21 00:01:05,770 --> 00:01:08,260 And so when you went to register for this workshop 22 00:01:08,260 --> 00:01:10,210 they gave you a homework assignment. 23 00:01:10,210 --> 00:01:11,890 I thought that was interesting. 24 00:01:11,890 --> 00:01:16,000 So my homework assignment was to go to the chemistry department 25 00:01:16,000 --> 00:01:18,490 and interview all the undergraduates that 26 00:01:18,490 --> 00:01:22,000 were members of underrepresented minority groups. 27 00:01:22,000 --> 00:01:24,670 And I discovered at the time, this was a number of years ago, 28 00:01:24,670 --> 00:01:27,460 there were two, and both of these students 29 00:01:27,460 --> 00:01:30,490 had transferred very recently to other departments. 30 00:01:30,490 --> 00:01:35,188 So that was not the best indication, 31 00:01:35,188 --> 00:01:37,480 but the students were very interested in talking to me. 32 00:01:37,480 --> 00:01:39,700 And I said, I want to hear about your experience. 33 00:01:39,700 --> 00:01:42,760 I want to think about what the department could do better. 34 00:01:42,760 --> 00:01:45,880 Again, this is an issue from the past. 35 00:01:45,880 --> 00:01:48,580 Things are a lot better right now. 36 00:01:48,580 --> 00:01:51,010 So I met with these students, and one in particular 37 00:01:51,010 --> 00:01:53,230 was just really engaged with me. 38 00:01:53,230 --> 00:01:55,870 They're like, yes, I want to help you figure out 39 00:01:55,870 --> 00:01:58,930 what's going on, and talking about their experience, 40 00:01:58,930 --> 00:02:02,990 and thought a lot about what could have been different. 41 00:02:02,990 --> 00:02:06,550 And finally, came up with this thing, 42 00:02:06,550 --> 00:02:13,810 and he said, I never had a TA that believed in me. 43 00:02:13,810 --> 00:02:14,880 And I just thought-- 44 00:02:14,880 --> 00:02:16,950 I was not expecting that response. 45 00:02:16,950 --> 00:02:18,660 Because I'd been working with TAs at that 46 00:02:18,660 --> 00:02:20,010 point for quite some time. 47 00:02:20,010 --> 00:02:22,260 And the TAs were really wonderful. 48 00:02:22,260 --> 00:02:24,000 They were engaged and excited. 49 00:02:24,000 --> 00:02:27,610 And I just thought, somehow the TAs-- 50 00:02:27,610 --> 00:02:28,960 there had been this disconnect. 51 00:02:28,960 --> 00:02:32,950 And I said, well, what did the TAs say to you? 52 00:02:32,950 --> 00:02:34,265 What happened? 53 00:02:34,265 --> 00:02:36,490 And he thought about it and tried 54 00:02:36,490 --> 00:02:40,880 to come up with an example, and then realized, 55 00:02:40,880 --> 00:02:47,000 it wasn't what they said, it was what they didn't say. 56 00:02:47,000 --> 00:02:49,010 And that just stuck with me. 57 00:02:49,010 --> 00:02:53,870 And I thought, his TAs probably had no idea 58 00:02:53,870 --> 00:02:55,640 that he was feeling this way. 59 00:02:55,640 --> 00:02:58,730 Here was this kid sitting in the classroom thinking, 60 00:02:58,730 --> 00:03:01,480 no one believes that I can do chemistry, 61 00:03:01,480 --> 00:03:04,730 and no one else was thinking that but him, probably. 62 00:03:04,730 --> 00:03:07,200 So I said, we need to talk about this. 63 00:03:07,200 --> 00:03:09,350 We need to talk about how we can all 64 00:03:09,350 --> 00:03:12,720 have classrooms where everyone can reach their full potential. 65 00:03:12,720 --> 00:03:14,900 So that's what this training is about today. 66 00:03:14,900 --> 00:03:16,940 We're going to start this discussion, how 67 00:03:16,940 --> 00:03:19,940 do we create classrooms where everyone can reach 68 00:03:19,940 --> 00:03:22,970 their full potential, where no one's worried, sitting in there 69 00:03:22,970 --> 00:03:25,550 really worried, about what someone 70 00:03:25,550 --> 00:03:27,530 might be thinking about them. 71 00:03:27,530 --> 00:03:29,900 So again, as a teacher, what I'd like to do starting out 72 00:03:29,900 --> 00:03:31,390 is give you the take home message. 73 00:03:31,390 --> 00:03:32,930 I like to give you the take-home message, 74 00:03:32,930 --> 00:03:34,280 tell you what I'm going to tell you, 75 00:03:34,280 --> 00:03:35,720 tell you the thing I'm going to tell you, 76 00:03:35,720 --> 00:03:37,220 and then tell you what I taught you. 77 00:03:37,220 --> 00:03:40,400 So there's a little bit of teacher training mixed in 78 00:03:40,400 --> 00:03:42,020 with this workshop as well. 79 00:03:42,020 --> 00:03:43,820 All right, so take-home message. 80 00:03:43,820 --> 00:03:46,850 So the take-home message is that understanding stereotype 81 00:03:46,850 --> 00:03:49,670 threat and wise criticism is essential for being 82 00:03:49,670 --> 00:03:51,950 a good mentor, supervisor, and teacher, 83 00:03:51,950 --> 00:03:55,310 and helps with being a good human being, I think, as well. 84 00:03:55,310 --> 00:03:56,240 So that's the message. 85 00:03:56,240 --> 00:03:59,620 And you might think, OK, I'm buying in, 86 00:03:59,620 --> 00:04:02,120 but I have no idea what you're talking about with stereotype 87 00:04:02,120 --> 00:04:02,620 threat. 88 00:04:02,620 --> 00:04:04,130 So let's go there first. 89 00:04:04,130 --> 00:04:04,630 All right. 90 00:04:04,630 --> 00:04:07,040 So let's look at some definitions. 91 00:04:07,040 --> 00:04:10,070 First, let's look at stereotype. 92 00:04:10,070 --> 00:04:14,390 So stereotype is a prevalent belief about a specific type 93 00:04:14,390 --> 00:04:17,010 of individuals or a way of doing things, 94 00:04:17,010 --> 00:04:20,360 which may or may not reflect reality. 95 00:04:20,360 --> 00:04:23,960 So most people can think of stereotypes. 96 00:04:23,960 --> 00:04:27,590 Stereotypes can be positive or negative. 97 00:04:27,590 --> 00:04:29,510 Most people think of negative stereotypes, 98 00:04:29,510 --> 00:04:31,340 but actually they can be positive. 99 00:04:31,340 --> 00:04:33,830 So an example of a positive stereotype 100 00:04:33,830 --> 00:04:37,140 is that MIT students are smart. 101 00:04:37,140 --> 00:04:41,570 I've been here almost 20 years, MIT students are smart. 102 00:04:41,570 --> 00:04:43,440 That's a positive stereotype. 103 00:04:43,440 --> 00:04:45,320 Stereotypes can be based in truth. 104 00:04:45,320 --> 00:04:49,330 That is pretty true that MIT students are smart. 105 00:04:49,330 --> 00:04:52,090 What about a negative stereotype? 106 00:04:52,090 --> 00:04:54,940 What is an example of a negative stereotype? 107 00:04:58,400 --> 00:04:59,000 Yes? 108 00:04:59,000 --> 00:05:00,625 AUDIENCE: So most people see that jocks 109 00:05:00,625 --> 00:05:02,120 are dumb or not so bright. 110 00:05:02,120 --> 00:05:04,330 So that's an example of negative stereotype. 111 00:05:04,330 --> 00:05:07,910 CATHY L. DRENNAN: Yes, and in my experience, that's not true. 112 00:05:07,910 --> 00:05:09,680 There are a lot of very athletic people-- 113 00:05:09,680 --> 00:05:12,140 something that some people don't know about MIT 114 00:05:12,140 --> 00:05:15,030 is that most people participate in sports here. 115 00:05:15,030 --> 00:05:17,030 So yeah, so a negative one. 116 00:05:17,030 --> 00:05:19,490 Again, they can be positive, they can be negative, 117 00:05:19,490 --> 00:05:22,460 they can be largely true, or not so true. 118 00:05:22,460 --> 00:05:24,980 I also want to introduce the idea of unconscious bias, 119 00:05:24,980 --> 00:05:27,230 because people are talking a lot about this. 120 00:05:27,230 --> 00:05:30,410 And actually, when I started this training, 121 00:05:30,410 --> 00:05:33,830 no one was talking about this term, and now it's the term. 122 00:05:33,830 --> 00:05:36,050 So let's talk about that. 123 00:05:36,050 --> 00:05:39,290 So it's defined as social stereotypes 124 00:05:39,290 --> 00:05:43,370 about certain groups that individuals form outside 125 00:05:43,370 --> 00:05:44,810 of their conscious awareness. 126 00:05:44,810 --> 00:05:47,420 So in other words, they don't know that they 127 00:05:47,420 --> 00:05:48,800 hold these stereotypes. 128 00:05:48,800 --> 00:05:49,915 Unconscious bias. 129 00:05:49,915 --> 00:05:51,290 And I think people like this term 130 00:05:51,290 --> 00:05:53,667 because it's takes the pressure off. 131 00:05:53,667 --> 00:05:56,000 I didn't know I have it, therefore, I'm not responsible. 132 00:05:56,000 --> 00:05:56,960 It's all good. 133 00:05:56,960 --> 00:05:59,283 But today, we're really getting in there, 134 00:05:59,283 --> 00:06:01,700 and I feel like I've been doing this training for a while, 135 00:06:01,700 --> 00:06:04,580 so I've become acutely aware of just how 136 00:06:04,580 --> 00:06:06,590 many stereotypes I have. 137 00:06:06,590 --> 00:06:10,303 So this is not about, oh, if it were true, freeing ourselves 138 00:06:10,303 --> 00:06:10,970 of these things. 139 00:06:10,970 --> 00:06:11,510 No. 140 00:06:11,510 --> 00:06:14,600 It's about making ourselves aware that we have them, 141 00:06:14,600 --> 00:06:18,840 and acting in such a way that we can counter the harm of it. 142 00:06:18,840 --> 00:06:19,340 All right. 143 00:06:19,340 --> 00:06:23,660 So stereotype threat is the perceived risk 144 00:06:23,660 --> 00:06:26,150 of confirming a negative stereotype. 145 00:06:26,150 --> 00:06:30,800 So say, as a female driver you do something stupid, 146 00:06:30,800 --> 00:06:33,060 and have a whole bunch of men honking at you. 147 00:06:33,060 --> 00:06:33,560 Right? 148 00:06:33,560 --> 00:06:35,980 You're like, oh, I didn't want to let the world-- 149 00:06:35,980 --> 00:06:40,010 you feel pressure of doing everything perfectly, 150 00:06:40,010 --> 00:06:43,990 because you don't want to play into the negative idea there. 151 00:06:43,990 --> 00:06:44,490 All right. 152 00:06:44,490 --> 00:06:47,300 So that's what stereotype threat is. 153 00:06:47,300 --> 00:06:50,030 And I think stereotype threat is still a good term for it, 154 00:06:50,030 --> 00:06:52,850 because unconscious bias threat doesn't really work. 155 00:06:52,850 --> 00:06:54,840 OK, so we're back to stereotypes. 156 00:06:54,840 --> 00:06:55,340 All right. 157 00:06:55,340 --> 00:06:57,210 So what are we going to talk about today? 158 00:06:57,210 --> 00:06:59,450 So we're going to talk more about these terms. 159 00:06:59,450 --> 00:07:01,040 We're going to talk about the fact 160 00:07:01,040 --> 00:07:04,580 that stereotype threat can lead to underperformance. 161 00:07:04,580 --> 00:07:08,263 So I'm going to show you data to support this idea. 162 00:07:08,263 --> 00:07:09,680 We're going to talk about the fact 163 00:07:09,680 --> 00:07:13,460 that stereotype threat can lead to the idea 164 00:07:13,460 --> 00:07:15,510 that you're being judged unfairly. 165 00:07:15,510 --> 00:07:18,200 Sometimes you might be judged unfairly, 166 00:07:18,200 --> 00:07:20,570 and sometimes you're not being judged unfairly, 167 00:07:20,570 --> 00:07:23,780 but stereotype threat can lead to that feeling 168 00:07:23,780 --> 00:07:26,670 whether or not it's true. 169 00:07:26,670 --> 00:07:31,950 Everyone can be a victim of stereotype threat, 170 00:07:31,950 --> 00:07:33,810 and everyone has stereotypes. 171 00:07:33,810 --> 00:07:36,590 So it affects all of us. 172 00:07:36,590 --> 00:07:39,200 We all hold stereotypes, we all can be 173 00:07:39,200 --> 00:07:41,390 victims of stereotype threat. 174 00:07:41,390 --> 00:07:43,970 This training is really for everyone in the room. 175 00:07:43,970 --> 00:07:46,430 And I have to say, this is not just about gender, 176 00:07:46,430 --> 00:07:48,320 it's not just about race. 177 00:07:48,320 --> 00:07:51,920 It's about anything that might make us feel different, 178 00:07:51,920 --> 00:07:56,060 anything that might make us feel under a microscope. 179 00:07:56,060 --> 00:07:59,150 If we're different in any way from the others around us, 180 00:07:59,150 --> 00:08:03,140 this can affect everybody. 181 00:08:03,140 --> 00:08:06,600 So it's for everyone. 182 00:08:06,600 --> 00:08:09,478 This is the bad news. 183 00:08:09,478 --> 00:08:10,520 What about the good news? 184 00:08:10,520 --> 00:08:12,970 There's always good news. 185 00:08:12,970 --> 00:08:15,030 There's wise criticism. 186 00:08:15,030 --> 00:08:20,390 So this has been shown to alleviate the negative aspects 187 00:08:20,390 --> 00:08:21,860 of stereotype threat. 188 00:08:21,860 --> 00:08:23,300 So what is it? 189 00:08:23,300 --> 00:08:27,680 So wise criticism is criticism where you explicitly 190 00:08:27,680 --> 00:08:30,267 let somebody know that they are capable, 191 00:08:30,267 --> 00:08:32,059 or you believe they're capable, of a higher 192 00:08:32,059 --> 00:08:33,890 level of achievement. 193 00:08:33,890 --> 00:08:37,820 And there is data to suggest this goes a long way. 194 00:08:37,820 --> 00:08:41,480 Again, if you can create this environment of trust, 195 00:08:41,480 --> 00:08:43,490 if the individuals in your classroom 196 00:08:43,490 --> 00:08:48,860 feel that you believe in them, you can criticize them-- 197 00:08:48,860 --> 00:08:50,750 I mean, this is important in Mentoring Lab. 198 00:08:50,750 --> 00:08:53,450 Criticism is how we learn. 199 00:08:53,450 --> 00:08:56,060 When we don't do well, we learn from that. 200 00:08:56,060 --> 00:08:57,740 It's all healthy and good. 201 00:08:57,740 --> 00:09:00,680 But we have to make sure that any criticism is delivered 202 00:09:00,680 --> 00:09:03,890 in such a way that the person recognizes 203 00:09:03,890 --> 00:09:05,960 that we believe in them. 204 00:09:05,960 --> 00:09:08,000 Again, back to the student who never had a TA-- 205 00:09:08,000 --> 00:09:11,660 we need everyone to know that we believe that the person is 206 00:09:11,660 --> 00:09:13,070 able to do the work. 207 00:09:13,070 --> 00:09:17,340 And so, that is one way to mitigate the negative effects. 208 00:09:17,340 --> 00:09:18,030 All right. 209 00:09:18,030 --> 00:09:21,570 So I don't know if I told you this ahead of time, 210 00:09:21,570 --> 00:09:24,250 but this is an interactive training. 211 00:09:24,250 --> 00:09:27,090 So we want everyone to participate, 212 00:09:27,090 --> 00:09:31,620 and think about how this has affected me or someone I know. 213 00:09:31,620 --> 00:09:33,690 So what we're going to do in a minute 214 00:09:33,690 --> 00:09:36,510 is you're going to pair share. 215 00:09:36,510 --> 00:09:40,830 Find a group of two or three, and turn to your neighbor 216 00:09:40,830 --> 00:09:43,680 and recall a time when you might have 217 00:09:43,680 --> 00:09:48,040 felt judged by some superficial characteristic. 218 00:09:48,040 --> 00:09:51,340 So it could be age, it could be anything. 219 00:09:51,340 --> 00:09:53,160 So think about that or-- 220 00:09:53,160 --> 00:09:56,030 and maybe and/or, you can do both-- 221 00:09:56,030 --> 00:09:59,888 you are worried about confirming a negative stereotype. 222 00:09:59,888 --> 00:10:00,930 So that's the assignment. 223 00:10:00,930 --> 00:10:02,370 Think about it a little bit. 224 00:10:02,370 --> 00:10:04,140 You'll talk to your neighbor. 225 00:10:04,140 --> 00:10:06,360 I'll give you about five minutes or so. 226 00:10:06,360 --> 00:10:08,730 And then we'll come back in a group, 227 00:10:08,730 --> 00:10:12,330 and hopefully some of you will feel comfortable 228 00:10:12,330 --> 00:10:15,060 sharing your experiences with a larger group, 229 00:10:15,060 --> 00:10:17,560 and we can talk about it. 230 00:10:17,560 --> 00:10:18,060 All right. 231 00:10:18,060 --> 00:10:21,540 So let's take a few minutes, find someone, share a story. 232 00:10:24,246 --> 00:10:29,610 AUDIENCE: So do you guys have any experiences like that? 233 00:10:29,610 --> 00:10:32,030 AUDIENCE: Yeah, so I guess one of mine 234 00:10:32,030 --> 00:10:34,610 that I started being really aware of when I started 235 00:10:34,610 --> 00:10:37,580 grad school was that I was really worried about being 236 00:10:37,580 --> 00:10:41,290 taken seriously as a scientist, if I had a meaningful hobbies 237 00:10:41,290 --> 00:10:43,760 outside of work. 238 00:10:43,760 --> 00:10:49,250 AUDIENCE: So I guess for me, when I came to MIT, 239 00:10:49,250 --> 00:10:52,400 I was really scared by the fact that there were 240 00:10:52,400 --> 00:10:55,800 few people that looked like me. 241 00:10:55,800 --> 00:10:59,630 And so, that put like a lot of pressure on me, 242 00:10:59,630 --> 00:11:06,650 and I felt that I had to carry a whole race on my back. 243 00:11:06,650 --> 00:11:09,650 And also, I can remember one meeting 244 00:11:09,650 --> 00:11:12,020 that I had with one of the professors, 245 00:11:12,020 --> 00:11:14,450 and she was telling me that, I know 246 00:11:14,450 --> 00:11:16,970 that you come from a small college, 247 00:11:16,970 --> 00:11:20,060 and that your classmates are going 248 00:11:20,060 --> 00:11:23,300 to be from Berkeley and Harvard, and so it might be 249 00:11:23,300 --> 00:11:25,480 right for you at the beginning. 250 00:11:25,480 --> 00:11:28,130 And I feel that really, really impacted 251 00:11:28,130 --> 00:11:31,820 how I performed in some of my classes, 252 00:11:31,820 --> 00:11:35,930 and I wish that I had never had that conversation. 253 00:11:35,930 --> 00:11:37,553 So, yeah. 254 00:11:37,553 --> 00:11:39,845 AUDIENCE: Yeah, being from a smaller school was harder. 255 00:11:39,845 --> 00:11:41,840 I was also from a smaller school, 256 00:11:41,840 --> 00:11:45,670 and in classes I was always thinking-- 257 00:11:45,670 --> 00:11:46,700 AUDIENCE: Yeah, yeah. 258 00:11:46,700 --> 00:11:48,650 Can I perform as well as other people? 259 00:11:48,650 --> 00:11:50,040 AUDIENCE: As well. 260 00:11:50,040 --> 00:11:50,890 Mm-hmm. 261 00:11:50,890 --> 00:11:51,530 AUDIENCE: Yeah. 262 00:11:51,530 --> 00:11:53,730 AUDIENCE: And then being told that you might not. 263 00:11:53,730 --> 00:11:54,905 AUDIENCE: Yeah, yeah, yeah. 264 00:11:54,905 --> 00:11:57,050 So yeah, so you question yourself. 265 00:11:57,050 --> 00:12:00,620 Sometimes you feel like you don't really belong here. 266 00:12:00,620 --> 00:12:03,170 And so, yeah, it can be hard for some people. 267 00:12:03,170 --> 00:12:07,040 AUDIENCE: Yeah, I had a very similar experience to yours. 268 00:12:07,040 --> 00:12:10,490 I was directly told that students from Puerto Rico 269 00:12:10,490 --> 00:12:13,270 don't really do that well in first year classes. 270 00:12:13,270 --> 00:12:17,160 And so, when I while I was never doing terribly in class, 271 00:12:17,160 --> 00:12:21,630 I felt like I didn't have to necessarily do well at all. 272 00:12:21,630 --> 00:12:25,960 So I would actually not try as hard as I should have too. 273 00:12:25,960 --> 00:12:26,700 Just do well. 274 00:12:26,700 --> 00:12:29,600 AUDIENCE: Because that's what they were expecting, right? 275 00:12:29,600 --> 00:12:31,955 AUDIENCE: Yeah. 276 00:12:31,955 --> 00:12:33,080 I think the other problem-- 277 00:12:33,080 --> 00:12:35,730 I've heard many people that have had the same experience. 278 00:12:35,730 --> 00:12:38,110 So it's sort of like-- 279 00:12:38,110 --> 00:12:41,790 AUDIENCE: Yeah, I wish that some of the professors 280 00:12:41,790 --> 00:12:45,890 would understand that they're not generally helping you 281 00:12:45,890 --> 00:12:47,780 when saying these things. 282 00:12:47,780 --> 00:12:51,295 And maybe a training like this wouldn't help them a lot. 283 00:12:51,295 --> 00:12:51,920 AUDIENCE: Yeah. 284 00:12:51,920 --> 00:12:54,890 AUDIENCE: Yeah, because were they trying to-- they're 285 00:12:54,890 --> 00:12:56,690 coming from a place of trying to help you. 286 00:12:56,690 --> 00:12:58,490 AUDIENCE: I think internally-- 287 00:12:58,490 --> 00:13:02,250 yeah, I think their intention was to help, 288 00:13:02,250 --> 00:13:03,650 but I think it's just-- 289 00:13:03,650 --> 00:13:05,255 AUDIENCE: Misguided. 290 00:13:05,255 --> 00:13:05,880 AUDIENCE: Yeah. 291 00:13:05,880 --> 00:13:06,505 AUDIENCE: Yeah. 292 00:13:06,505 --> 00:13:07,250 AUDIENCE: Yeah. 293 00:13:07,250 --> 00:13:09,357 AUDIENCE: Yeah, it's true. 294 00:13:09,357 --> 00:13:10,940 AUDIENCE: So a time that I was worried 295 00:13:10,940 --> 00:13:13,730 about confirming a negative stereotype was when I first 296 00:13:13,730 --> 00:13:15,290 started graduate school. 297 00:13:15,290 --> 00:13:18,770 I was coming from a primarily undergraduate institution. 298 00:13:18,770 --> 00:13:21,920 I had never been at a big research university. 299 00:13:21,920 --> 00:13:24,080 And so, I didn't really know what to expect 300 00:13:24,080 --> 00:13:26,180 or what I was getting myself into. 301 00:13:26,180 --> 00:13:30,110 And I guess, when people first explained to me 302 00:13:30,110 --> 00:13:33,440 what the expectations were in terms of how much I should 303 00:13:33,440 --> 00:13:36,380 work, and what vacation policies were, 304 00:13:36,380 --> 00:13:38,810 the first time they told me I literally laughed out loud. 305 00:13:38,810 --> 00:13:41,210 I didn't think they could possibly be serious. 306 00:13:41,210 --> 00:13:45,560 And then I realized, whoa, I might be in over my head. 307 00:13:45,560 --> 00:13:48,580 And once I realized that, I was like, oh, man. 308 00:13:48,580 --> 00:13:51,080 People are going to think that I'm not serious about my job, 309 00:13:51,080 --> 00:13:52,413 and I don't know what I'm doing. 310 00:13:52,413 --> 00:13:55,370 So I had to pretend all of a sudden 311 00:13:55,370 --> 00:13:57,920 that I was all focused on science, because I didn't want 312 00:13:57,920 --> 00:14:00,230 people to think that I wasn't taking it seriously 313 00:14:00,230 --> 00:14:01,880 because of my background. 314 00:14:01,880 --> 00:14:05,100 AUDIENCE: Yeah, no, I feel the same way coming to MIT. 315 00:14:05,100 --> 00:14:07,190 Especially, coming in for me, confirming 316 00:14:07,190 --> 00:14:09,590 the negative stereotype, also just being 317 00:14:09,590 --> 00:14:12,560 judged for my physical attributes as a woman of color. 318 00:14:12,560 --> 00:14:18,380 I feel like there's a lot of stereotypes, negative, around 319 00:14:18,380 --> 00:14:19,718 my ethnicity and my race. 320 00:14:19,718 --> 00:14:21,760 And one of the things that I heard a lot of times 321 00:14:21,760 --> 00:14:24,700 it's the idea that, you speak well, 322 00:14:24,700 --> 00:14:26,990 or you're articulate for a black woman. 323 00:14:26,990 --> 00:14:28,640 And this is not only my story. 324 00:14:28,640 --> 00:14:30,740 I've had a number of my colleagues 325 00:14:30,740 --> 00:14:32,660 say the same thing to me. 326 00:14:32,660 --> 00:14:35,270 So it's something that's just constantly being battled 327 00:14:35,270 --> 00:14:37,340 against a negative stereotype. 328 00:14:37,340 --> 00:14:40,010 And also this idea that coming in-- 329 00:14:40,010 --> 00:14:41,870 a combination of my, I like to call 330 00:14:41,870 --> 00:14:45,450 it an intersectionality of all my personalities 331 00:14:45,450 --> 00:14:46,940 and all my identities. 332 00:14:46,940 --> 00:14:48,680 So I'm a woman of color. 333 00:14:48,680 --> 00:14:49,820 I am from the Caribbean. 334 00:14:49,820 --> 00:14:51,645 I went to a small school, but I'm not only 335 00:14:51,645 --> 00:14:53,020 just a small liberal arts school, 336 00:14:53,020 --> 00:14:55,850 I went to a small HBCU, which is a historically Black college 337 00:14:55,850 --> 00:14:56,570 and university. 338 00:14:56,570 --> 00:14:59,540 And I feel like all those combinations also mix. 339 00:14:59,540 --> 00:15:01,100 Get into a classroom, and being one 340 00:15:01,100 --> 00:15:03,650 of the only persons of color makes 341 00:15:03,650 --> 00:15:05,548 me feel extra pressure for confirming 342 00:15:05,548 --> 00:15:07,340 these negative stereotypes that are thought 343 00:15:07,340 --> 00:15:09,470 against people of color. 344 00:15:09,470 --> 00:15:11,713 So in the classrooms I try not to ask questions, 345 00:15:11,713 --> 00:15:13,130 because I don't want to out myself 346 00:15:13,130 --> 00:15:17,467 as not being smart, or not as much as my other colleagues 347 00:15:17,467 --> 00:15:19,050 who might have went to bigger schools, 348 00:15:19,050 --> 00:15:22,470 and have had targeted classes on this particular subject, 349 00:15:22,470 --> 00:15:23,420 for example. 350 00:15:23,420 --> 00:15:27,460 So it's been definitely the idea of being the "face of the race" 351 00:15:27,460 --> 00:15:30,100 also, because you don't want to spoil it for other people who 352 00:15:30,100 --> 00:15:30,670 come along. 353 00:15:30,670 --> 00:15:32,680 So it's been a little bit like you 354 00:15:32,680 --> 00:15:35,800 feel like you have to not ask questions, 355 00:15:35,800 --> 00:15:38,050 you're not out yourself. 356 00:15:38,050 --> 00:15:40,990 Anything you say might prove that negative stereotype, 357 00:15:40,990 --> 00:15:43,930 and it's a lot of pressure to do. 358 00:15:43,930 --> 00:15:44,430 So yeah. 359 00:15:44,430 --> 00:15:46,180 AUDIENCE: Yeah, totally. 360 00:15:46,180 --> 00:15:48,940 AUDIENCE: I can't necessarily relate to your experience, 361 00:15:48,940 --> 00:15:52,360 but the flip side of the coin on that is I 362 00:15:52,360 --> 00:15:53,867 do have one sliver of an experience 363 00:15:53,867 --> 00:15:55,450 where I kind of feel like I can relate 364 00:15:55,450 --> 00:15:56,658 to what you're talking about. 365 00:15:56,658 --> 00:15:59,270 And that's when we're having conversations about diversity, 366 00:15:59,270 --> 00:16:01,330 I feel like I don't want to say something 367 00:16:01,330 --> 00:16:04,900 that might sound ignorant, or naive, or uninformed. 368 00:16:04,900 --> 00:16:07,900 Because I feel like if I end up saying something stupid 369 00:16:07,900 --> 00:16:11,510 and offending someone, I set the conversation back a step, 370 00:16:11,510 --> 00:16:14,020 and then I end up confirming the stereotype 371 00:16:14,020 --> 00:16:17,620 that all white men don't care about improving diversity 372 00:16:17,620 --> 00:16:18,490 in science. 373 00:16:18,490 --> 00:16:20,650 AUDIENCE: Yeah, well, that's very understandable. 374 00:16:20,650 --> 00:16:21,970 And I feel like it's very crippling too, 375 00:16:21,970 --> 00:16:24,440 because he might have really good experiences that you want 376 00:16:24,440 --> 00:16:26,440 to share, and good contributions that you 377 00:16:26,440 --> 00:16:28,080 could make in that sense. 378 00:16:28,080 --> 00:16:30,770 And you can't do it because of that negative stereotype. 379 00:16:30,770 --> 00:16:32,020 You're really confirming that. 380 00:16:32,020 --> 00:16:34,270 So it's definitely very crippling. 381 00:16:34,270 --> 00:16:37,900 AUDIENCE: Yeah, I think it is detrimental to the whole cause, 382 00:16:37,900 --> 00:16:39,515 because it takes everybody, right? 383 00:16:39,515 --> 00:16:40,640 AUDIENCE: It does, it does. 384 00:16:40,640 --> 00:16:41,973 AUDIENCE: For all participating. 385 00:16:41,973 --> 00:16:43,510 AUDIENCE: It is. 386 00:16:43,510 --> 00:16:46,240 AUDIENCE: OK, so I think-- 387 00:16:46,240 --> 00:16:46,740 let me see. 388 00:16:46,740 --> 00:16:48,115 Thinking about a time when I felt 389 00:16:48,115 --> 00:16:50,020 judged about a negative stereotype. 390 00:16:50,020 --> 00:16:52,540 So I'm pretty short. 391 00:16:52,540 --> 00:16:55,720 I don't know if you noticed when I walked in, but I think that-- 392 00:16:55,720 --> 00:16:56,785 and I look pretty young. 393 00:16:56,785 --> 00:16:58,660 My sister's two years younger, but I'm always 394 00:16:58,660 --> 00:17:01,720 considered the younger sister, and I 395 00:17:01,720 --> 00:17:04,569 think that that's led me to always 396 00:17:04,569 --> 00:17:08,710 be really afraid of confirming that women are weak, 397 00:17:08,710 --> 00:17:11,450 or not able to do things on their own. 398 00:17:11,450 --> 00:17:17,050 So I'm very conscious of not asking for help 399 00:17:17,050 --> 00:17:18,190 with certain situations. 400 00:17:18,190 --> 00:17:20,140 If I can't reach things or things like that, 401 00:17:20,140 --> 00:17:21,760 I know where all the step-stools are. 402 00:17:21,760 --> 00:17:26,740 Or I've been known to climb on shelves in grocery stores 403 00:17:26,740 --> 00:17:28,150 instead of asking for help. 404 00:17:28,150 --> 00:17:31,360 And it's one of those situations where I'm very much afraid, 405 00:17:31,360 --> 00:17:34,030 and I often lead with the fact that I have a black belt 406 00:17:34,030 --> 00:17:37,870 in Taekwondo to kind of help paint 407 00:17:37,870 --> 00:17:41,230 this picture that I actually can do things for myself. 408 00:17:41,230 --> 00:17:42,063 So, yeah. 409 00:17:42,063 --> 00:17:43,480 AUDIENCE: I feel that, because I'm 410 00:17:43,480 --> 00:17:45,340 literally the same way, including the black belt. 411 00:17:45,340 --> 00:17:45,840 (LAUGHS) 412 00:17:45,840 --> 00:17:46,470 AUDIENCE: Yeah. 413 00:17:46,470 --> 00:17:48,040 AUDIENCE: It's one thing I lead with. 414 00:17:48,040 --> 00:17:50,017 AUDIENCE: It's something that kind 415 00:17:50,017 --> 00:17:52,100 of shocks people, because they're like, oh, you're 416 00:17:52,100 --> 00:17:53,918 so little. 417 00:17:53,918 --> 00:17:54,460 I don't know. 418 00:17:54,460 --> 00:17:56,590 I feel like I'm always so worried about the fact 419 00:17:56,590 --> 00:17:58,090 that just because I'm so little-- 420 00:17:58,090 --> 00:17:59,465 and I've always been this little, 421 00:17:59,465 --> 00:18:02,530 it's not like I was the tallest person in my class-- 422 00:18:02,530 --> 00:18:05,960 that I need to be able to show that I can take care of myself, 423 00:18:05,960 --> 00:18:10,330 and be independent, because of this stereotype that women 424 00:18:10,330 --> 00:18:13,220 can't, and need a man to make sure 425 00:18:13,220 --> 00:18:15,160 that things are OK for them. 426 00:18:15,160 --> 00:18:16,090 So, yeah. 427 00:18:16,090 --> 00:18:17,590 That's where I'm at with that. 428 00:18:17,590 --> 00:18:19,750 AUDIENCE: Oh, I'm sorry. 429 00:18:19,750 --> 00:18:20,500 AUDIENCE: It's OK. 430 00:18:20,500 --> 00:18:21,660 I can't get taller. 431 00:18:21,660 --> 00:18:24,890 AUDIENCE: Yeah, well, when I was six years old, 432 00:18:24,890 --> 00:18:27,430 I actually used to race cars. 433 00:18:27,430 --> 00:18:29,830 And I remember there was a day when 434 00:18:29,830 --> 00:18:32,290 I should've gotten second place in a race, 435 00:18:32,290 --> 00:18:35,020 but because the three boys in front of me 436 00:18:35,020 --> 00:18:38,890 crashed right before the end of the race, they gave me fifth, 437 00:18:38,890 --> 00:18:42,160 because they were in second and third and fourth 438 00:18:42,160 --> 00:18:44,410 before the flag went down. 439 00:18:44,410 --> 00:18:45,850 But then I crossed the line first, 440 00:18:45,850 --> 00:18:47,142 so I should have gotten second. 441 00:18:47,142 --> 00:18:49,930 Anyway, after this whole situation is over, 442 00:18:49,930 --> 00:18:52,810 one of the boys came up to me and was like, 443 00:18:52,810 --> 00:18:54,610 oh, well, you're a girl. 444 00:18:54,610 --> 00:18:56,440 Of course you can't get second place. 445 00:18:56,440 --> 00:18:58,690 And I was like, excuse me? 446 00:18:58,690 --> 00:19:00,572 Yes, that was over 20 years ago. 447 00:19:00,572 --> 00:19:02,280 AUDIENCE: And that was when you were six. 448 00:19:02,280 --> 00:19:03,160 AUDIENCE: That was when I was six. 449 00:19:03,160 --> 00:19:04,285 AUDIENCE: How old was the-- 450 00:19:04,285 --> 00:19:06,820 AUDIENCE: I think he was seven. 451 00:19:06,820 --> 00:19:07,990 So, yeah. 452 00:19:07,990 --> 00:19:08,890 It's still there. 453 00:19:08,890 --> 00:19:10,290 So, yeah. 454 00:19:10,290 --> 00:19:10,901 Good times. 455 00:19:10,901 --> 00:19:12,343 AUDIENCE: Yeah. 456 00:19:12,343 --> 00:19:13,510 CATHY L. DRENNAN: All right. 457 00:19:13,510 --> 00:19:14,962 Let's come back as a group. 458 00:19:14,962 --> 00:19:16,420 There was a lot of good discussion. 459 00:19:16,420 --> 00:19:20,040 I can't wait to hear what you're going to say. 460 00:19:20,040 --> 00:19:22,780 But I thought I would start out by sharing a story 461 00:19:22,780 --> 00:19:24,510 to get us going. 462 00:19:24,510 --> 00:19:28,390 So in the first category, a time that you were judged. 463 00:19:28,390 --> 00:19:31,510 So I think back to a time when I was 464 00:19:31,510 --> 00:19:34,300 graduating from Vassar College. 465 00:19:34,300 --> 00:19:38,290 These are the old days, where if you announced who got an award, 466 00:19:38,290 --> 00:19:41,860 they'd post a piece of paper in the student union. 467 00:19:41,860 --> 00:19:44,800 There was no email thing to send out to people. 468 00:19:44,800 --> 00:19:46,890 And so, everyone gathered around the list 469 00:19:46,890 --> 00:19:49,540 of people who had been named Phi Beta Kappa. 470 00:19:49,540 --> 00:19:53,230 And I was really excited that I had been named Phi Beta Kappa. 471 00:19:53,230 --> 00:19:56,638 And so, I'm leaving after having looked at the list, 472 00:19:56,638 --> 00:19:58,180 and I saw someone who I hadn't really 473 00:19:58,180 --> 00:19:59,630 talked to since freshman year. 474 00:19:59,630 --> 00:20:01,810 He was in my dorm, and it was one of those things 475 00:20:01,810 --> 00:20:04,990 like freshman year, where the entire floor of your dorm 476 00:20:04,990 --> 00:20:06,820 like goes everywhere together. 477 00:20:06,820 --> 00:20:08,680 You go to the dining hall together, 478 00:20:08,680 --> 00:20:11,320 you go to parties together, because there's 479 00:20:11,320 --> 00:20:12,100 this herd effect. 480 00:20:12,100 --> 00:20:15,040 Anyway, this guy Phillip had been in my herd, 481 00:20:15,040 --> 00:20:17,380 but I'd never actually talked to him. 482 00:20:17,380 --> 00:20:21,400 So he walks up to me and he looks at me, and he said, 483 00:20:21,400 --> 00:20:24,480 you were on that list. 484 00:20:24,480 --> 00:20:28,990 And I said, yes. 485 00:20:28,990 --> 00:20:33,240 And he just looks at me, and then he said, 486 00:20:33,240 --> 00:20:39,050 it never occurred to me that you were smart. 487 00:20:39,050 --> 00:20:41,430 [SOFT LAUGHTER] 488 00:20:41,430 --> 00:20:44,760 I mean you could just see the wheels turning 489 00:20:44,760 --> 00:20:49,700 like, her name is on the list! 490 00:20:49,700 --> 00:20:53,330 Her name is on that list! 491 00:20:53,330 --> 00:20:56,070 She's smart? 492 00:20:56,070 --> 00:20:59,660 And I just thought like, what did I do that this was such-- 493 00:20:59,660 --> 00:21:00,680 It's like, oh, whatever. 494 00:21:00,680 --> 00:21:02,680 If they saw it, they might not have ever thought 495 00:21:02,680 --> 00:21:04,550 about it one way or another, but clearly, he 496 00:21:04,550 --> 00:21:09,080 had formed an extremely strong opinion that there was no way 497 00:21:09,080 --> 00:21:11,990 that I could be smart, and this was just 498 00:21:11,990 --> 00:21:15,710 messing with his worldview, the fact my name was on the list. 499 00:21:15,710 --> 00:21:17,930 So what was it about me that was just 500 00:21:17,930 --> 00:21:22,280 so inconceivable to this person that I could be smart? 501 00:21:22,280 --> 00:21:29,000 OK, so in the second category, pretend you're at MIT. 502 00:21:29,000 --> 00:21:32,270 In the Chemistry Department at the point where I was coming up 503 00:21:32,270 --> 00:21:34,580 for tenure, there was one female professor 504 00:21:34,580 --> 00:21:36,560 who had gotten tenure, and then they 505 00:21:36,560 --> 00:21:38,660 had hired two more in with tenure. 506 00:21:38,660 --> 00:21:40,310 So I was number two. 507 00:21:40,310 --> 00:21:41,570 Case study. 508 00:21:41,570 --> 00:21:43,130 Would she get tenure? 509 00:21:43,130 --> 00:21:44,780 And I felt like they were all watching. 510 00:21:44,780 --> 00:21:46,670 Is she going to meetings? 511 00:21:46,670 --> 00:21:49,160 How many papers has she published? 512 00:21:49,160 --> 00:21:50,180 Is she going to make it? 513 00:21:50,180 --> 00:21:50,930 What do you think? 514 00:21:50,930 --> 00:21:51,860 Lay in the bets. 515 00:21:51,860 --> 00:21:52,640 What are the odds? 516 00:21:52,640 --> 00:21:53,120 I don't know. 517 00:21:53,120 --> 00:21:54,110 There probably were odds. 518 00:21:54,110 --> 00:21:55,235 I don't know what they are. 519 00:21:55,235 --> 00:21:56,750 I never want to know. 520 00:21:56,750 --> 00:22:00,350 But I just felt like, man, my not getting tenure 521 00:22:00,350 --> 00:22:03,170 it's not really about me anymore. 522 00:22:03,170 --> 00:22:06,410 It's, can women get tenure? 523 00:22:06,410 --> 00:22:08,610 And it was all riding on my shoulders. 524 00:22:08,610 --> 00:22:10,430 I was going to provide the answer, 525 00:22:10,430 --> 00:22:12,552 and people would make decisions about their lives, 526 00:22:12,552 --> 00:22:14,260 I was afraid, based on whether it worked. 527 00:22:14,260 --> 00:22:15,385 Oh, it didn't work for her. 528 00:22:15,385 --> 00:22:17,750 I'm not sure I want to go forward. 529 00:22:17,750 --> 00:22:19,220 That is a lot of pressure when you 530 00:22:19,220 --> 00:22:24,540 feel like you're carrying other people besides yourself around. 531 00:22:24,540 --> 00:22:26,160 So that's an example for me. 532 00:22:26,160 --> 00:22:26,660 All right. 533 00:22:26,660 --> 00:22:29,780 So you don't have to have as profound examples, 534 00:22:29,780 --> 00:22:32,090 but everyone has some Phillip in their life, right? 535 00:22:32,090 --> 00:22:33,260 Has somebody like that. 536 00:22:33,260 --> 00:22:35,170 So let's hear. 537 00:22:35,170 --> 00:22:39,194 Who wants to share about this? 538 00:22:39,194 --> 00:22:40,423 Yeah? 539 00:22:40,423 --> 00:22:42,340 AUDIENCE: So in addition to being a scientist, 540 00:22:42,340 --> 00:22:45,730 I'm also a musician, and when I got to grad school 541 00:22:45,730 --> 00:22:48,400 I felt really insecure about having this hobby, 542 00:22:48,400 --> 00:22:53,410 and thought that I might not be seen as serious of a scientist 543 00:22:53,410 --> 00:22:56,390 because I like to play music as well. 544 00:22:56,390 --> 00:23:00,560 And so, I downplayed my hobby, and didn't join ensembles 545 00:23:00,560 --> 00:23:01,060 as much. 546 00:23:01,060 --> 00:23:03,310 But even then, when I started in grad school 547 00:23:03,310 --> 00:23:04,870 participating in an ensemble, I was 548 00:23:04,870 --> 00:23:06,790 really worried I'd confirm the stereotype 549 00:23:06,790 --> 00:23:09,880 that just because I was also a musician, 550 00:23:09,880 --> 00:23:11,833 I was less serious of a scientist. 551 00:23:11,833 --> 00:23:14,000 CATHY L. DRENNAN: Yeah, thanks for bringing that up. 552 00:23:14,000 --> 00:23:16,910 I think that's a really great example. 553 00:23:16,910 --> 00:23:20,080 So it's just we have these ideas, 554 00:23:20,080 --> 00:23:25,780 and it's like we have these square holes and round holes, 555 00:23:25,780 --> 00:23:28,930 and we're trying to bat everybody into it. 556 00:23:28,930 --> 00:23:30,970 There's many different things that people 557 00:23:30,970 --> 00:23:32,260 can do if they're scientists. 558 00:23:32,260 --> 00:23:34,840 There's many different hobbies and other interests 559 00:23:34,840 --> 00:23:36,340 that they can have, and we shouldn't 560 00:23:36,340 --> 00:23:38,500 have such a narrow definition. 561 00:23:38,500 --> 00:23:39,865 So someone else? 562 00:23:39,865 --> 00:23:41,680 I heard lots of discussion going on. 563 00:23:41,680 --> 00:23:42,730 Yeah? 564 00:23:42,730 --> 00:23:45,220 AUDIENCE: So in terms of being judged 565 00:23:45,220 --> 00:23:49,280 by someone for a superficial characteristic, as an African-- 566 00:23:49,280 --> 00:23:50,860 Afro-Caribbean I should say-- 567 00:23:50,860 --> 00:23:55,090 woman in the scientific setting, I feel like one thing 568 00:23:55,090 --> 00:23:59,260 I get a lot is that, you speak very well. 569 00:23:59,260 --> 00:24:01,840 And usually I'm like, for? 570 00:24:01,840 --> 00:24:03,550 Why? 571 00:24:03,550 --> 00:24:07,170 But I think it's definitely I'm clearly a minority, 572 00:24:07,170 --> 00:24:12,670 so basically there's some negative stereotypes 573 00:24:12,670 --> 00:24:16,070 about whole women of color should present themselves, 574 00:24:16,070 --> 00:24:18,380 and I don't fit in to that stereotype. 575 00:24:18,380 --> 00:24:23,230 So every time I do give a talk or I do express my opinions, 576 00:24:23,230 --> 00:24:25,090 people say I'm very well-spoken. 577 00:24:25,090 --> 00:24:28,480 And this is not only my own case. 578 00:24:28,480 --> 00:24:29,900 I've heard a number of my friends 579 00:24:29,900 --> 00:24:32,530 say the same things, even sometimes from their advisors 580 00:24:32,530 --> 00:24:35,390 and people who have gone through a lot of students. 581 00:24:35,390 --> 00:24:39,310 So that's one case where I feel like I've 582 00:24:39,310 --> 00:24:43,868 been judged superficially for a superficial characteristic. 583 00:24:43,868 --> 00:24:45,910 Another thing of confirming a negative stereotype 584 00:24:45,910 --> 00:24:47,540 also comes back to my ethnicity. 585 00:24:51,240 --> 00:24:56,590 You mentioned feeling like you carry the weight of persons 586 00:24:56,590 --> 00:25:00,130 on your back, and thinking about being, 587 00:25:00,130 --> 00:25:02,320 again, an African-American woman, most of the time 588 00:25:02,320 --> 00:25:04,720 the only one in a room. 589 00:25:04,720 --> 00:25:05,220 Not today. 590 00:25:05,220 --> 00:25:06,690 [LAUGHTER] 591 00:25:06,690 --> 00:25:07,345 That's great. 592 00:25:07,345 --> 00:25:09,720 But most of the time it's being the only one in the room. 593 00:25:09,720 --> 00:25:12,250 And especially coming from a small college, 594 00:25:12,250 --> 00:25:14,650 I also felt coming into my first chemistry classes, 595 00:25:14,650 --> 00:25:17,170 there was this pressure that I put on myself to not 596 00:25:17,170 --> 00:25:20,047 confirm these negative stereotypes about people 597 00:25:20,047 --> 00:25:21,880 of color in terms of that they're not smart, 598 00:25:21,880 --> 00:25:23,680 or they're not good at math or science, 599 00:25:23,680 --> 00:25:25,310 and I didn't belong there. 600 00:25:25,310 --> 00:25:29,170 And it's not because people around me said anything. 601 00:25:29,170 --> 00:25:30,940 It was definitely internal that I 602 00:25:30,940 --> 00:25:33,650 felt like I had to not answer a question, 603 00:25:33,650 --> 00:25:37,060 or not ask a question because I didn't want to look as if I 604 00:25:37,060 --> 00:25:39,940 didn't know something, or all my classmates 605 00:25:39,940 --> 00:25:41,560 knew it and I didn't know it. 606 00:25:41,560 --> 00:25:44,170 So it's that weight of one, not confirming 607 00:25:44,170 --> 00:25:46,270 that negative stereotype, and also not spoiling it 608 00:25:46,270 --> 00:25:48,640 for everybody else who comes after me 609 00:25:48,640 --> 00:25:51,867 who is a URM from an HBCU. 610 00:25:51,867 --> 00:25:52,825 CATHY L. DRENNAN: Yeah. 611 00:25:52,825 --> 00:25:54,370 Those are both really great examples. 612 00:25:54,370 --> 00:25:56,350 Thank you for sharing those. 613 00:25:56,350 --> 00:25:58,720 I think that really gets to the heart of what 614 00:25:58,720 --> 00:26:01,990 we're trying to talk about. 615 00:26:01,990 --> 00:26:06,858 Feeling judged, and you get these compliments-- 616 00:26:06,858 --> 00:26:08,650 and I've gotten this for some of the talks. 617 00:26:08,650 --> 00:26:12,220 It's like, that was actually pretty good! 618 00:26:12,220 --> 00:26:13,970 What was your expectation ahead of time? 619 00:26:13,970 --> 00:26:15,610 Or, you spoke well! 620 00:26:15,610 --> 00:26:19,790 Like, thank you. 621 00:26:19,790 --> 00:26:26,410 Yeah, and that extra pressure to-- 622 00:26:26,410 --> 00:26:28,040 who else is coming along with us? 623 00:26:28,040 --> 00:26:29,470 So thank you for sharing those. 624 00:26:29,470 --> 00:26:31,540 That's really great. 625 00:26:31,540 --> 00:26:32,235 Maybe one more. 626 00:26:32,235 --> 00:26:34,360 Does someone else have something you want to share? 627 00:26:34,360 --> 00:26:36,280 Yeah? 628 00:26:36,280 --> 00:26:38,530 AUDIENCE: When I participate in conversations 629 00:26:38,530 --> 00:26:41,140 about improving diversity in science, 630 00:26:41,140 --> 00:26:45,070 a lot of times I'll worry that if I ask an uninformed question 631 00:26:45,070 --> 00:26:49,000 or make a naive suggestion that I'll set the conversation back 632 00:26:49,000 --> 00:26:51,250 or offend somebody, and ultimately 633 00:26:51,250 --> 00:26:54,880 confirm the stereotype that white male scientists aren't 634 00:26:54,880 --> 00:26:57,250 serious about improving diversity in science. 635 00:26:57,250 --> 00:26:59,290 So then I'll tell myself, just sit back 636 00:26:59,290 --> 00:27:00,905 and listen to other people. 637 00:27:00,905 --> 00:27:02,530 And then as soon as I start doing that, 638 00:27:02,530 --> 00:27:05,350 I'll start to worry, how are people perceiving this? 639 00:27:05,350 --> 00:27:08,020 Do I look sullen and like I don't care about this? 640 00:27:08,020 --> 00:27:09,670 And then, again, I worry that I'm 641 00:27:09,670 --> 00:27:11,140 confirming a different stereotype 642 00:27:11,140 --> 00:27:12,380 about white men in science. 643 00:27:12,380 --> 00:27:12,405 [SOFT LAUGHTER] 644 00:27:12,405 --> 00:27:14,620 So I think ultimately, it's important for me 645 00:27:14,620 --> 00:27:19,960 to remember that my voice is overrepresented in science, 646 00:27:19,960 --> 00:27:23,290 and I should make a concerted effort to actively listen 647 00:27:23,290 --> 00:27:26,350 to other people's perspectives, but also 648 00:27:26,350 --> 00:27:29,553 recognize that by spending so much mental energy worrying 649 00:27:29,553 --> 00:27:31,470 about how other people might be perceiving me, 650 00:27:31,470 --> 00:27:35,260 I'm ultimately undermining my own ability to make 651 00:27:35,260 --> 00:27:38,710 a positive contribution towards improving diversity in science. 652 00:27:38,710 --> 00:27:40,585 CATHY L. DRENNAN: Thank you for sharing that. 653 00:27:40,585 --> 00:27:43,150 I think that's a really good point. 654 00:27:43,150 --> 00:27:47,830 I've had people, when I've done this training before, a man say 655 00:27:47,830 --> 00:27:51,550 that he felt very much under sort of stereotype threat 656 00:27:51,550 --> 00:27:53,300 when he was taking a woman's study course, 657 00:27:53,300 --> 00:27:55,330 and he was the only guy in the class. 658 00:27:55,330 --> 00:27:57,940 And that sometimes he felt like if he spoke up 659 00:27:57,940 --> 00:28:00,160 and what he said, that he was going to really 660 00:28:00,160 --> 00:28:04,710 be judged by it, and it was just a very uncomfortable situation. 661 00:28:04,710 --> 00:28:07,180 And I really feel like what we want to do here 662 00:28:07,180 --> 00:28:08,350 is start the discussion. 663 00:28:08,350 --> 00:28:10,780 Everyone's voice is so important. 664 00:28:10,780 --> 00:28:12,400 And sometimes it drives me crazy when 665 00:28:12,400 --> 00:28:15,160 I'm hearing about women in science, it's like, OK, 666 00:28:15,160 --> 00:28:16,540 you, woman, go fix this. 667 00:28:16,540 --> 00:28:18,080 It's like, I can't do this. 668 00:28:18,080 --> 00:28:20,620 I think we all need to be talking about this 669 00:28:20,620 --> 00:28:21,850 and doing this together. 670 00:28:21,850 --> 00:28:24,520 These problems are not problems for women to solve, 671 00:28:24,520 --> 00:28:26,440 or for people of color to solve. 672 00:28:26,440 --> 00:28:28,210 We want to improve science. 673 00:28:28,210 --> 00:28:30,040 We all need to be part of this discussion, 674 00:28:30,040 --> 00:28:32,230 and everyone's voice is so important. 675 00:28:32,230 --> 00:28:35,500 So we want to have a situation where everyone feels 676 00:28:35,500 --> 00:28:37,780 like they can speak, and everyone 677 00:28:37,780 --> 00:28:40,060 is going to listen to what other people have to say. 678 00:28:40,060 --> 00:28:41,800 So very on point. 679 00:28:41,800 --> 00:28:43,010 Thank you very much. 680 00:28:43,010 --> 00:28:43,510 All right. 681 00:28:43,510 --> 00:28:46,840 So I think we should get back, and we 682 00:28:46,840 --> 00:28:49,480 want to go back because I have some data to show you. 683 00:28:49,480 --> 00:28:51,710 And this is MIT, and we're scientists, 684 00:28:51,710 --> 00:28:54,080 so let's look at some data. 685 00:28:54,080 --> 00:28:54,580 All right. 686 00:28:54,580 --> 00:28:58,450 So there is a book by Claude Steele called 687 00:28:58,450 --> 00:29:00,910 Whistling Vivaldi, and Other Clues as to How 688 00:29:00,910 --> 00:29:03,070 Stereotypes Affect Us. 689 00:29:03,070 --> 00:29:06,460 Claude Steele is often viewed as the father of stereotype 690 00:29:06,460 --> 00:29:09,490 threat, really coming up with this concept 691 00:29:09,490 --> 00:29:11,620 and making people aware of it. 692 00:29:11,620 --> 00:29:14,095 And his book has a lot of data in it. 693 00:29:14,095 --> 00:29:15,220 A lot of different studies. 694 00:29:15,220 --> 00:29:18,460 He summarizes studies that many different people have done. 695 00:29:18,460 --> 00:29:23,620 So some of the studies are more social studies, some of them 696 00:29:23,620 --> 00:29:26,260 are more in the neuroscience area. 697 00:29:26,260 --> 00:29:29,920 So some studies that he describes actually 698 00:29:29,920 --> 00:29:33,880 look at brain waves and blood flow in the brain. 699 00:29:33,880 --> 00:29:36,970 And so, they will put people under stereotype threat, 700 00:29:36,970 --> 00:29:39,910 and there's a variety of ways you can do it, 701 00:29:39,910 --> 00:29:42,850 poking at those negative stereotypes 702 00:29:42,850 --> 00:29:47,350 that we talked about, and then actually watch blood flow 703 00:29:47,350 --> 00:29:48,790 or brain activity. 704 00:29:48,790 --> 00:29:52,270 And you can see movement from the part of the brain 705 00:29:52,270 --> 00:29:54,370 where it's about logic and reasoning, 706 00:29:54,370 --> 00:29:56,320 to, when people are under stereotype 707 00:29:56,320 --> 00:30:00,310 threat, the part of the brain that's about fear, about fight 708 00:30:00,310 --> 00:30:01,610 or flight. 709 00:30:01,610 --> 00:30:04,360 And so when you're in the classroom, 710 00:30:04,360 --> 00:30:07,640 the goal is all the students their brain is with logic, 711 00:30:07,640 --> 00:30:08,140 right? 712 00:30:08,140 --> 00:30:09,970 They're processing the information 713 00:30:09,970 --> 00:30:11,290 and they're learning. 714 00:30:11,290 --> 00:30:12,940 You don't want people in your classroom 715 00:30:12,940 --> 00:30:17,020 where the brain is very anxious, and worrying about 716 00:30:17,020 --> 00:30:19,400 whether you belong and whether you can do it. 717 00:30:19,400 --> 00:30:23,110 So there's an actual biological reason why stereotype threat 718 00:30:23,110 --> 00:30:24,508 can lead to underperformance. 719 00:30:24,508 --> 00:30:26,800 Your brain's not doing what your brain should be doing. 720 00:30:26,800 --> 00:30:30,970 It should be in the logic area, not in the fear area. 721 00:30:30,970 --> 00:30:35,560 So this is one thing that's talked about in his book. 722 00:30:35,560 --> 00:30:36,160 So anyway. 723 00:30:36,160 --> 00:30:38,560 So some of the studies that he did, 724 00:30:38,560 --> 00:30:42,220 it was groups of female undergrads. 725 00:30:42,220 --> 00:30:44,800 Stanford quite a good school. 726 00:30:44,800 --> 00:30:47,960 People who get in there, very smart people. 727 00:30:47,960 --> 00:30:52,030 And so, he looked at the SAT scores of a group of women, 728 00:30:52,030 --> 00:30:54,220 and then he divided them into two groups. 729 00:30:54,220 --> 00:30:56,440 And they should have equal math abilities 730 00:30:56,440 --> 00:30:58,820 based on their SAT scores. 731 00:30:58,820 --> 00:31:02,170 So for one group, he either reminded 732 00:31:02,170 --> 00:31:04,510 them of their gender before the exam, and in some case, 733 00:31:04,510 --> 00:31:05,890 didn't say anything at all. 734 00:31:05,890 --> 00:31:08,290 But in the second group, he told them 735 00:31:08,290 --> 00:31:11,350 that the exam had no gender bias. 736 00:31:11,350 --> 00:31:13,210 And so, he would start out saying, 737 00:31:13,210 --> 00:31:16,000 well, you may have heard that women often underperform 738 00:31:16,000 --> 00:31:19,390 on SATs in the math, but we've been working here, 739 00:31:19,390 --> 00:31:21,550 and we've created a math exam that 740 00:31:21,550 --> 00:31:25,900 has no gender bias, so everyone can do up 741 00:31:25,900 --> 00:31:28,510 to their full ability. 742 00:31:28,510 --> 00:31:30,610 There's nothing holding you back. 743 00:31:30,610 --> 00:31:33,610 And what was really interesting that this group performed 744 00:31:33,610 --> 00:31:37,228 very well compared to the other group that was either reminded 745 00:31:37,228 --> 00:31:38,770 of their gender, or sometimes nothing 746 00:31:38,770 --> 00:31:40,330 was said about gender at all. 747 00:31:40,330 --> 00:31:42,700 So when these women thought that they 748 00:31:42,700 --> 00:31:45,010 could do as well as they could do, 749 00:31:45,010 --> 00:31:48,820 they over-performed or outperformed the other group. 750 00:31:48,820 --> 00:31:50,290 So there's a number of these kinds 751 00:31:50,290 --> 00:31:52,150 of studies done in different ways 752 00:31:52,150 --> 00:31:55,540 showing again that if you feel like you 753 00:31:55,540 --> 00:31:59,300 can reach your full performance, you get there. 754 00:31:59,300 --> 00:32:03,790 But if you're reminded of these issues, 755 00:32:03,790 --> 00:32:06,483 and that you might be confirming a negative stereotype, 756 00:32:06,483 --> 00:32:07,900 or you're worried about confirming 757 00:32:07,900 --> 00:32:11,490 a negative stereotype, it can lead to poor performance. 758 00:32:11,490 --> 00:32:11,990 All right. 759 00:32:11,990 --> 00:32:16,370 So stereotype threat can lead to underperformance, 760 00:32:16,370 --> 00:32:21,430 which is a problem in a classroom, for sure. 761 00:32:21,430 --> 00:32:23,200 The other point that I brought up before, 762 00:32:23,200 --> 00:32:26,110 and I want to come back to, is that it can also 763 00:32:26,110 --> 00:32:30,130 lead to a feeling of being judged unfairly. 764 00:32:30,130 --> 00:32:31,810 And this is one of my favorite studies, 765 00:32:31,810 --> 00:32:35,300 and it's back from the 1980s. 766 00:32:35,300 --> 00:32:38,740 So in this study they said, all right, we 767 00:32:38,740 --> 00:32:41,770 want to address the differences-- how people 768 00:32:41,770 --> 00:32:46,570 are treated if they have some kind of physical disfigurement, 769 00:32:46,570 --> 00:32:48,873 in particular, a scar on your face. 770 00:32:48,873 --> 00:32:50,290 So he said, what we're going to do 771 00:32:50,290 --> 00:32:53,230 is we're going to bring in a makeup artist from Hollywood, 772 00:32:53,230 --> 00:32:56,410 they're going to put a scar on your face, 773 00:32:56,410 --> 00:32:59,260 and then we're going to send you into this room 774 00:32:59,260 --> 00:33:01,840 and we're going to watch the interactions. 775 00:33:01,840 --> 00:33:04,210 And we want you to tell us, are you 776 00:33:04,210 --> 00:33:05,890 being treated differently because you 777 00:33:05,890 --> 00:33:07,810 have this scar on your face? 778 00:33:07,810 --> 00:33:10,390 So the volunteers sat in front of the mirror. 779 00:33:10,390 --> 00:33:13,000 They had the makeup artist come and this scar 780 00:33:13,000 --> 00:33:14,560 was put on their face. 781 00:33:14,560 --> 00:33:17,350 And then right before they were supposed to go in, 782 00:33:17,350 --> 00:33:18,920 the makeup artist came back and said, 783 00:33:18,920 --> 00:33:21,503 we're just going to touch it up, make sure you're ready to go. 784 00:33:21,503 --> 00:33:25,720 They removed it entirely, but the person had no idea. 785 00:33:25,720 --> 00:33:28,660 They thought that the scar was still on the face. 786 00:33:28,660 --> 00:33:34,360 Went into the room, there was a discussion, came out. 787 00:33:34,360 --> 00:33:38,520 The volunteer said, oh, I was treated entirely differently 788 00:33:38,520 --> 00:33:40,582 with this scar on my face. 789 00:33:40,582 --> 00:33:41,790 And other people had watched. 790 00:33:41,790 --> 00:33:43,560 There was nothing going on. 791 00:33:43,560 --> 00:33:45,060 But they were just sensing-- 792 00:33:45,060 --> 00:33:47,490 they were looking for signs that they 793 00:33:47,490 --> 00:33:49,170 were being treated unfairly. 794 00:33:49,170 --> 00:33:51,420 And when you look for things, sometimes you 795 00:33:51,420 --> 00:33:56,190 can find them or interpret them, when they don't really exist. 796 00:33:56,190 --> 00:33:59,160 So right now, some people are sitting, 797 00:33:59,160 --> 00:34:02,580 might have their arms crossed a little bit. 798 00:34:02,580 --> 00:34:06,780 And if I were an insecure person, I could say, 799 00:34:06,780 --> 00:34:09,060 that person's sitting there is thinking 800 00:34:09,060 --> 00:34:13,199 there is nothing this woman has to tell me that I want to hear. 801 00:34:13,199 --> 00:34:15,219 And they just are sitting like that. 802 00:34:15,219 --> 00:34:18,600 But then I also know it's a little cold in this room. 803 00:34:18,600 --> 00:34:20,520 Someone might just be a little bit cold, 804 00:34:20,520 --> 00:34:22,800 and that's why they're sitting like this, right? 805 00:34:22,800 --> 00:34:25,530 You can interpret all sorts of things. 806 00:34:25,530 --> 00:34:29,489 If you're looking for a sign that somebody is questioning 807 00:34:29,489 --> 00:34:33,977 you, you might be able to find it even when it's not there. 808 00:34:33,977 --> 00:34:35,310 And sometimes it's really there. 809 00:34:35,310 --> 00:34:36,810 Sometimes people are judging. 810 00:34:36,810 --> 00:34:37,350 Oh, yeah. 811 00:34:37,350 --> 00:34:37,850 [LAUGHTER] 812 00:34:37,850 --> 00:34:39,830 We can have stories later. 813 00:34:39,830 --> 00:34:42,065 But sometimes they're not. 814 00:34:42,065 --> 00:34:43,690 So I just want to give you one example. 815 00:34:43,690 --> 00:34:46,540 So this is the other counter-- 816 00:34:46,540 --> 00:34:49,449 a guide to reducing stereotype threat 817 00:34:49,449 --> 00:34:52,000 and maximizing student performance, also 818 00:34:52,000 --> 00:34:55,780 a guide to Cathy's most embarrassing moments. 819 00:34:55,780 --> 00:34:59,680 But there was one time I was meeting with a professor, 820 00:34:59,680 --> 00:35:00,740 I was pre-tenure. 821 00:35:00,740 --> 00:35:02,920 Again, I wanted to get tenure. 822 00:35:02,920 --> 00:35:04,420 Very nervous about this whole thing. 823 00:35:04,420 --> 00:35:06,753 And so, this person in my field, likely a letter-writer, 824 00:35:06,753 --> 00:35:07,990 is coming to my office. 825 00:35:07,990 --> 00:35:09,220 And I was really excited. 826 00:35:09,220 --> 00:35:11,950 So I was like, I had results that he was going to like. 827 00:35:11,950 --> 00:35:13,600 I knew he would like these results. 828 00:35:13,600 --> 00:35:15,940 I printed him out, like these slides. 829 00:35:15,940 --> 00:35:17,710 I had everything all ready. 830 00:35:17,710 --> 00:35:19,450 I thought through my spiel. 831 00:35:19,450 --> 00:35:20,220 I was psyched. 832 00:35:20,220 --> 00:35:21,490 He he came. 833 00:35:21,490 --> 00:35:22,990 And so he came in, he's like, I'm 834 00:35:22,990 --> 00:35:24,460 so glad you're here visiting. 835 00:35:24,460 --> 00:35:25,043 This is great. 836 00:35:25,043 --> 00:35:26,877 I have these new results I want to show you, 837 00:35:26,877 --> 00:35:27,730 and I pull this out. 838 00:35:27,730 --> 00:35:29,970 And he goes, you know, I was sort of wondering, 839 00:35:29,970 --> 00:35:31,510 how's your experience been here? 840 00:35:31,510 --> 00:35:32,470 I was like, good, good. 841 00:35:32,470 --> 00:35:33,100 Very good. 842 00:35:33,100 --> 00:35:34,790 Now, we had this result-- 843 00:35:34,790 --> 00:35:37,720 and he's like, but I had some questions. 844 00:35:37,720 --> 00:35:41,190 Do you feel like you've been treated OK? 845 00:35:41,190 --> 00:35:42,420 30 minutes went by. 846 00:35:42,420 --> 00:35:45,660 He did not look at a single result that I had. 847 00:35:45,660 --> 00:35:47,910 And I just thought, this guy has just 848 00:35:47,910 --> 00:35:52,500 decided that there is no result that I could possibly 849 00:35:52,500 --> 00:35:56,310 have that would be interesting enough to pay attention to. 850 00:35:56,310 --> 00:35:59,220 And so, he's just going to spend this half hour chatting 851 00:35:59,220 --> 00:36:03,060 about this and that, and things about me being a junior faculty 852 00:36:03,060 --> 00:36:09,590 member, whatever, and just clearly disrespectful of me. 853 00:36:09,590 --> 00:36:11,140 So that was my take. 854 00:36:11,140 --> 00:36:14,710 Find out later he has a son that's starting as an Assistant 855 00:36:14,710 --> 00:36:15,730 Professor. 856 00:36:15,730 --> 00:36:19,510 He wants to give his son advice, but he hasn't been an Assistant 857 00:36:19,510 --> 00:36:20,770 Professor for a long time. 858 00:36:20,770 --> 00:36:22,060 Things have changed. 859 00:36:22,060 --> 00:36:24,935 So he said, when I go to talk to Cathy Drennan, 860 00:36:24,935 --> 00:36:26,560 I'm going to ask her a lot of questions 861 00:36:26,560 --> 00:36:28,270 about being an Assistant Professor, 862 00:36:28,270 --> 00:36:31,810 because she has been so hugely successful that I 863 00:36:31,810 --> 00:36:34,870 want to take how she's done things 864 00:36:34,870 --> 00:36:37,240 and tell my son about it. 865 00:36:37,240 --> 00:36:41,680 So I could not have been more wrong. 866 00:36:41,680 --> 00:36:44,230 I thought he was judging me unfairly. 867 00:36:44,230 --> 00:36:47,840 Turns out, that was not the case at all. 868 00:36:47,840 --> 00:36:49,780 And so, because other people-- 869 00:36:49,780 --> 00:36:54,850 I've one male professor explain to another male professor 870 00:36:54,850 --> 00:36:56,950 my data without involving me in the discussion, 871 00:36:56,950 --> 00:36:58,810 because what could I possibly know about it? 872 00:36:58,810 --> 00:37:01,390 So I've had these experiences, but then I'm 873 00:37:01,390 --> 00:37:03,770 looking for these experiences in other places, 874 00:37:03,770 --> 00:37:05,410 and sometimes they're not there at all. 875 00:37:05,410 --> 00:37:07,600 Sometimes a person is being respectful. 876 00:37:07,600 --> 00:37:11,260 So when you feel like you're under stereotype threat, 877 00:37:11,260 --> 00:37:16,270 you can sometimes take a situation entirely incorrectly. 878 00:37:16,270 --> 00:37:19,290 Now, he could have also said, hey, Cathy, 879 00:37:19,290 --> 00:37:20,770 you've been so successful. 880 00:37:20,770 --> 00:37:22,420 Let me ask you questions. 881 00:37:22,420 --> 00:37:25,210 That could have-- but whatever. 882 00:37:25,210 --> 00:37:26,320 Whatever. 883 00:37:26,320 --> 00:37:27,970 So I think this is something also 884 00:37:27,970 --> 00:37:31,620 to think about, that people are looking to each other like, 885 00:37:31,620 --> 00:37:32,375 do you trust? 886 00:37:32,375 --> 00:37:33,250 Do you believe in me? 887 00:37:33,250 --> 00:37:35,020 Do you trust what I'm going to say? 888 00:37:35,020 --> 00:37:37,210 We're trying to get a sense of this. 889 00:37:37,210 --> 00:37:40,480 And sometimes we may get it wrong, 890 00:37:40,480 --> 00:37:42,760 and sometimes it may not be there, 891 00:37:42,760 --> 00:37:45,530 but everyone in the classroom is not the same. 892 00:37:45,530 --> 00:37:47,030 Even if you treat everyone the same, 893 00:37:47,030 --> 00:37:48,430 they're not always the same, because people 894 00:37:48,430 --> 00:37:49,840 are coming in with baggage. 895 00:37:49,840 --> 00:37:52,510 And I've got baggage, let me tell you. 896 00:37:52,510 --> 00:37:53,620 I got some baggage. 897 00:37:53,620 --> 00:37:56,350 I'll put my baggage up against your baggage, 898 00:37:56,350 --> 00:37:59,130 but everyone is coming in with something. 899 00:37:59,130 --> 00:37:59,820 All right. 900 00:37:59,820 --> 00:38:03,090 So I think you already know the answer to this one. 901 00:38:03,090 --> 00:38:05,490 Who can be a victim of stereotype threat? 902 00:38:05,490 --> 00:38:06,802 ENTIRE AUDIENCE: Everyone! 903 00:38:06,802 --> 00:38:08,135 CATHY L. DRENNAN: Yes, everyone. 904 00:38:08,135 --> 00:38:09,920 [LAUGHTER] 905 00:38:09,920 --> 00:38:13,280 In fact, studies show that it's the strongest students that 906 00:38:13,280 --> 00:38:14,870 are the most affected. 907 00:38:14,870 --> 00:38:17,660 So if you care about what people think of you, 908 00:38:17,660 --> 00:38:20,510 if you care about doing well, if you're ambitious, 909 00:38:20,510 --> 00:38:24,440 if you want to do important things in your life, 910 00:38:24,440 --> 00:38:26,970 you care about these kinds of things. 911 00:38:26,970 --> 00:38:28,880 And if you're at a place that has 912 00:38:28,880 --> 00:38:31,880 a good reputation, like MIT, that 913 00:38:31,880 --> 00:38:33,830 can lead to imposter syndrome. 914 00:38:33,830 --> 00:38:36,620 So most you've probably heard of imposter syndrome 915 00:38:36,620 --> 00:38:39,680 before, but this idea that somehow 916 00:38:39,680 --> 00:38:42,230 you were only accepted to MIT-- 917 00:38:42,230 --> 00:38:43,953 your name is like someone else, and they 918 00:38:43,953 --> 00:38:45,120 weren't going to accept you. 919 00:38:45,120 --> 00:38:48,140 But then they made this mistake and then somehow you got in, 920 00:38:48,140 --> 00:38:50,450 said the person who was below you in the line when 921 00:38:50,450 --> 00:38:51,860 they were supposed to get in. 922 00:38:51,860 --> 00:38:54,710 And that everyone else here is brilliant except for you, 923 00:38:54,710 --> 00:38:56,500 and someday that people are going 924 00:38:56,500 --> 00:39:00,320 to figure out that you're trying to look like a nerd-- 925 00:39:00,320 --> 00:39:01,340 or a horse. 926 00:39:01,340 --> 00:39:04,400 You know, you've got your horse stockings on 927 00:39:04,400 --> 00:39:06,260 and your horse cape. 928 00:39:06,260 --> 00:39:08,270 Or your zebra cape and your zebra 929 00:39:08,270 --> 00:39:10,650 stockings, but you're not really a zebra. 930 00:39:10,650 --> 00:39:14,240 And that you're wearing your nerdiest clothes, 931 00:39:14,240 --> 00:39:17,210 but you're really not an MIT nerd. 932 00:39:17,210 --> 00:39:21,770 So this is something that so many people at top universities 933 00:39:21,770 --> 00:39:25,400 are affected by, and this is really important 934 00:39:25,400 --> 00:39:28,280 when people are, again, under stereotype threat. 935 00:39:28,280 --> 00:39:30,920 They're feeling like, man, I'm the only one who doesn't really 936 00:39:30,920 --> 00:39:32,480 belong here. 937 00:39:32,480 --> 00:39:34,990 So imposter syndrome. 938 00:39:34,990 --> 00:39:35,490 OK. 939 00:39:35,490 --> 00:39:38,900 Another teaching tip, because you'll all be teaching. 940 00:39:38,900 --> 00:39:41,360 Before you teach on something, look up 941 00:39:41,360 --> 00:39:42,600 what it says on Wikipedia. 942 00:39:42,600 --> 00:39:45,890 Just double check what it says. 943 00:39:45,890 --> 00:39:49,220 So what do you think Wikipedia says 944 00:39:49,220 --> 00:39:53,360 of the people who are most affected by imposter syndrome? 945 00:39:53,360 --> 00:39:55,490 What particular group? 946 00:39:55,490 --> 00:39:56,090 Any guesses? 947 00:40:00,790 --> 00:40:01,290 Men? 948 00:40:01,290 --> 00:40:02,207 AUDIENCE: [INAUDIBLE]. 949 00:40:02,207 --> 00:40:03,990 [LAUGHTER] 950 00:40:03,990 --> 00:40:05,090 Graduate students. 951 00:40:05,090 --> 00:40:07,750 Someone has been googling Wikipedia. 952 00:40:07,750 --> 00:40:11,000 That is, in fact, correct. 953 00:40:11,000 --> 00:40:15,590 So a lot of people say, oh, it's women. 954 00:40:15,590 --> 00:40:20,740 But the data actually shows that men and women suffer equally, 955 00:40:20,740 --> 00:40:22,280 which is not really my experience. 956 00:40:22,280 --> 00:40:25,680 I think that if this is true, it's just that women talk about 957 00:40:25,680 --> 00:40:28,050 it all the time and men don't. 958 00:40:28,050 --> 00:40:30,240 But apparently, the data shows that it's 959 00:40:30,240 --> 00:40:33,060 equal among men and women. 960 00:40:33,060 --> 00:40:36,060 And it is commonly associated with academics 961 00:40:36,060 --> 00:40:39,720 and widely found among graduate students. 962 00:40:39,720 --> 00:40:42,120 So again, it's people who want to do 963 00:40:42,120 --> 00:40:44,400 important things with her life. 964 00:40:44,400 --> 00:40:45,300 Graduate school. 965 00:40:45,300 --> 00:40:47,340 Anyone who's in graduate school-- 966 00:40:47,340 --> 00:40:49,800 that's a pretty big deal that you're in graduate school. 967 00:40:49,800 --> 00:40:52,853 So you really you care about what people think. 968 00:40:52,853 --> 00:40:54,270 You care about whether you belong. 969 00:40:54,270 --> 00:40:56,850 You're thinking, how did I end up in graduate school, 970 00:40:56,850 --> 00:41:00,640 how did I end up at MIT, and do I really belong here? 971 00:41:00,640 --> 00:41:04,380 So these are natural things for people to think. 972 00:41:04,380 --> 00:41:07,470 And I will tell you that a number of faculty 973 00:41:07,470 --> 00:41:10,990 have reported that yes they suffer from imposter syndrome. 974 00:41:10,990 --> 00:41:15,180 I think anyone who is honest will say that they do. 975 00:41:15,180 --> 00:41:17,570 So this is a big problem. 976 00:41:17,570 --> 00:41:18,080 All right. 977 00:41:18,080 --> 00:41:21,760 So let's do another exercise. 978 00:41:21,760 --> 00:41:24,200 You might have wondered about the title of Claude Steele's 979 00:41:24,200 --> 00:41:27,110 book that I mentioned, Whistling Vivaldi. 980 00:41:27,110 --> 00:41:29,420 Where does that title come from? 981 00:41:29,420 --> 00:41:31,460 So he explains the title, which is a good thing, 982 00:41:31,460 --> 00:41:35,120 because it is an unusual title. 983 00:41:35,120 --> 00:41:38,390 So he said that it comes from a story 984 00:41:38,390 --> 00:41:42,170 that he heard of this young African-American man who 985 00:41:42,170 --> 00:41:44,370 was in the South Side of Chicago, 986 00:41:44,370 --> 00:41:46,425 and this man was a musician. 987 00:41:46,425 --> 00:41:48,050 And he was walking one night, and there 988 00:41:48,050 --> 00:41:52,610 was an elderly couple walking on the street right near him. 989 00:41:52,610 --> 00:41:56,570 And they saw him walking near them, 990 00:41:56,570 --> 00:41:59,623 and he saw them walking faster. 991 00:41:59,623 --> 00:42:01,790 And they were an elderly couple, so they were really 992 00:42:01,790 --> 00:42:04,070 trying to walk fast. 993 00:42:04,070 --> 00:42:08,060 And he felt kind of bad because they were kind of old. 994 00:42:08,060 --> 00:42:10,430 And so, he wanted to kind of yell out at them, 995 00:42:10,430 --> 00:42:12,350 you do not need to worry about me. 996 00:42:12,350 --> 00:42:14,990 I'm not going to do anything bad to you. 997 00:42:14,990 --> 00:42:20,000 But yelling that probably would be even more terrifying. 998 00:42:20,000 --> 00:42:22,540 So we thought about it and he said, 999 00:42:22,540 --> 00:42:24,550 I'm going to start whistling Vivaldi, 1000 00:42:24,550 --> 00:42:28,000 actually whistling the music of Vivaldi. 1001 00:42:28,000 --> 00:42:30,890 And so, he started whistling the music and the couple 1002 00:42:30,890 --> 00:42:33,280 slowed their pace, because they thought, 1003 00:42:33,280 --> 00:42:35,740 this is someone who is classically trained 1004 00:42:35,740 --> 00:42:38,620 or loves classical music. 1005 00:42:38,620 --> 00:42:40,510 Not so much associated with someone who's 1006 00:42:40,510 --> 00:42:42,430 going to steal my handbag. 1007 00:42:42,430 --> 00:42:44,140 And so, they relaxed. 1008 00:42:44,140 --> 00:42:49,150 So in this book, Steele asserts that we all whistle 1009 00:42:49,150 --> 00:42:50,720 Vivaldi from time to time. 1010 00:42:50,720 --> 00:42:53,530 We all try to fit in and make other people more 1011 00:42:53,530 --> 00:42:55,480 comfortable with us. 1012 00:42:55,480 --> 00:42:59,350 So this is a little bit of a harder thing to think about. 1013 00:42:59,350 --> 00:43:01,840 Have you ever whistled Vivaldi? 1014 00:43:01,840 --> 00:43:04,590 Have you ever tried to fit in? 1015 00:43:04,590 --> 00:43:06,722 And let me just give you an example that I heard 1016 00:43:06,722 --> 00:43:07,930 to help you think about this. 1017 00:43:07,930 --> 00:43:11,230 So one student said that he was actually 1018 00:43:11,230 --> 00:43:14,140 from a very religious family, and he 1019 00:43:14,140 --> 00:43:17,680 was used to praying before eating a meal. 1020 00:43:17,680 --> 00:43:22,030 But in the lunch room of a science building somehow 1021 00:43:22,030 --> 00:43:25,350 that felt weird to pray before eating. 1022 00:43:25,350 --> 00:43:28,840 A lot of people have this idea that if you're 1023 00:43:28,840 --> 00:43:32,170 very religious that somehow that's 1024 00:43:32,170 --> 00:43:35,200 counter to being a scientist, because you have 1025 00:43:35,200 --> 00:43:39,850 faith vs. Scientific facts. 1026 00:43:39,850 --> 00:43:43,960 And he wasn't sure how people would judge him if they 1027 00:43:43,960 --> 00:43:46,060 knew about his religion. 1028 00:43:46,060 --> 00:43:48,490 So even though he really did want to pray, 1029 00:43:48,490 --> 00:43:50,110 he stopped doing it because he just 1030 00:43:50,110 --> 00:43:51,733 thought he wouldn't fit in. 1031 00:43:51,733 --> 00:43:53,650 It would make other people feel uncomfortable. 1032 00:43:53,650 --> 00:43:55,600 He wasn't sure how others would feel, 1033 00:43:55,600 --> 00:43:58,720 and if they would accept him the same way as a scientist 1034 00:43:58,720 --> 00:44:00,590 if he prayed before a meal. 1035 00:44:00,590 --> 00:44:02,620 So that's an example of whistling Vivaldi. 1036 00:44:02,620 --> 00:44:03,970 So think about it for a minute. 1037 00:44:03,970 --> 00:44:05,525 Find your partner. 1038 00:44:05,525 --> 00:44:07,900 Talk to them about whether this has ever happened to you, 1039 00:44:07,900 --> 00:44:09,400 and then we'll come back together 1040 00:44:09,400 --> 00:44:10,630 as a group in a few minutes. 1041 00:44:15,380 --> 00:44:19,210 AUDIENCE: Yeah, so when I think about whistling Vivaldi, 1042 00:44:19,210 --> 00:44:22,280 for me, the point where I feel like I'm really 1043 00:44:22,280 --> 00:44:24,850 changing or trying to conform to an appearance 1044 00:44:24,850 --> 00:44:28,640 is pretty much any time after 9/11. 1045 00:44:28,640 --> 00:44:31,040 When I go through the airports I'm 1046 00:44:31,040 --> 00:44:35,660 always conscious of what people might be thinking about 1047 00:44:35,660 --> 00:44:38,600 based on my skin color. 1048 00:44:38,600 --> 00:44:45,230 And so, when I go through, every day right before I travel, 1049 00:44:45,230 --> 00:44:46,850 I shave my beard off. 1050 00:44:46,850 --> 00:44:51,560 I try to be dressed up a little nicer. 1051 00:44:51,560 --> 00:44:53,780 And I even feel myself being a little extra friendly 1052 00:44:53,780 --> 00:44:57,530 to TSA gate agents, because I feel 1053 00:44:57,530 --> 00:45:00,290 like there's this negative stereotype hanging over me. 1054 00:45:00,290 --> 00:45:06,470 And I feel like even though it's on my mind, 1055 00:45:06,470 --> 00:45:09,020 and it feels sometimes like it might be on everybody else's. 1056 00:45:09,020 --> 00:45:12,765 I feel like maybe my ID's looked at a little differently, 1057 00:45:12,765 --> 00:45:13,640 and things like that. 1058 00:45:13,640 --> 00:45:16,990 So I make sure they can hear my American accent, 1059 00:45:16,990 --> 00:45:20,352 make sure they don't see any facial hair. 1060 00:45:20,352 --> 00:45:21,060 Things like that. 1061 00:45:21,060 --> 00:45:24,920 I try to be extra nice, extra polite. 1062 00:45:24,920 --> 00:45:26,830 Just feeling a little more nervous, I think, 1063 00:45:26,830 --> 00:45:29,698 is really the cause of that as well. 1064 00:45:29,698 --> 00:45:31,490 AUDIENCE: Yeah, that's really good example. 1065 00:45:31,490 --> 00:45:32,198 AUDIENCE: Mm-hmm. 1066 00:45:32,198 --> 00:45:34,620 AUDIENCE: Mine would be in professional settings, 1067 00:45:34,620 --> 00:45:39,080 especially at conferences or any presentation of some sort, 1068 00:45:39,080 --> 00:45:42,740 I try to cover my tattoos the best way I can. 1069 00:45:42,740 --> 00:45:45,020 And I also have a nose ring, and so sometimes I 1070 00:45:45,020 --> 00:45:47,400 would wear like a clear nose ring 1071 00:45:47,400 --> 00:45:49,880 so people only if they're really up close to me 1072 00:45:49,880 --> 00:45:50,960 can see that I have one. 1073 00:45:50,960 --> 00:45:54,050 And that's just to mitigate any stereotype threats 1074 00:45:54,050 --> 00:45:55,610 that people have. 1075 00:45:55,610 --> 00:45:58,550 Because you walk into a room and you 1076 00:45:58,550 --> 00:46:02,000 have tattoos visible, or your piercings, 1077 00:46:02,000 --> 00:46:03,410 especially on your face, and that 1078 00:46:03,410 --> 00:46:06,410 can lead people to think differently about you 1079 00:46:06,410 --> 00:46:07,800 and what you're capable of doing. 1080 00:46:07,800 --> 00:46:09,217 And so, I just want to lessen that 1081 00:46:09,217 --> 00:46:12,815 and have people look past that. 1082 00:46:12,815 --> 00:46:13,440 AUDIENCE: Yeah. 1083 00:46:13,440 --> 00:46:13,690 AUDIENCE: Yeah. 1084 00:46:13,690 --> 00:46:15,240 AUDIENCE: I think that's a really good example. 1085 00:46:15,240 --> 00:46:16,823 I mean, it's all the same thing, is we 1086 00:46:16,823 --> 00:46:20,267 feel like our appearances are telling something, 1087 00:46:20,267 --> 00:46:22,100 and we're really worried that the message is 1088 00:46:22,100 --> 00:46:23,305 going to be negative there. 1089 00:46:26,438 --> 00:46:26,980 AUDIENCE: OK. 1090 00:46:26,980 --> 00:46:30,590 So when have we whistled Vivaldi? 1091 00:46:30,590 --> 00:46:31,660 AUDIENCE: Yeah. 1092 00:46:31,660 --> 00:46:35,140 I think I especially do it whenever 1093 00:46:35,140 --> 00:46:36,730 I'm with friends and family. 1094 00:46:36,730 --> 00:46:43,430 So since English isn't my native language, 1095 00:46:43,430 --> 00:46:45,790 but I feel like I can speak it pretty well, 1096 00:46:45,790 --> 00:46:49,930 so then whenever I go back home, my friends and family 1097 00:46:49,930 --> 00:46:53,710 don't always speak English, I actually 1098 00:46:53,710 --> 00:46:55,690 intentionally mispronounce certain words just 1099 00:46:55,690 --> 00:46:58,420 to keep that connection with them. 1100 00:46:58,420 --> 00:46:59,490 AUDIENCE: Yeah. 1101 00:46:59,490 --> 00:47:03,480 AUDIENCE: Since I've been gone for like a few months, 1102 00:47:03,480 --> 00:47:07,420 and seeing me every so often, I just 1103 00:47:07,420 --> 00:47:10,485 want them to feel like I'm still the same me I was. 1104 00:47:10,485 --> 00:47:11,110 AUDIENCE: Yeah. 1105 00:47:11,110 --> 00:47:12,485 AUDIENCE: And then when I'm here, 1106 00:47:12,485 --> 00:47:16,430 I always try to pronounce everything super well, 1107 00:47:16,430 --> 00:47:19,851 so that's not an issue when someone's talking to me. 1108 00:47:19,851 --> 00:47:20,476 AUDIENCE: Yeah. 1109 00:47:20,476 --> 00:47:21,250 AUDIENCE: Yeah. 1110 00:47:21,250 --> 00:47:23,940 AUDIENCE: [INAUDIBLE] similar? 1111 00:47:23,940 --> 00:47:26,920 AUDIENCE: Yeah, so I guess for me, 1112 00:47:26,920 --> 00:47:29,900 one of the big things about grad school 1113 00:47:29,900 --> 00:47:35,350 was that I sometimes felt pretty isolated as one of very 1114 00:47:35,350 --> 00:47:37,080 few women in our department. 1115 00:47:37,080 --> 00:47:42,280 And so, over time, I realized I started dressing more and more 1116 00:47:42,280 --> 00:47:45,760 like a man, to fit in and to be taken 1117 00:47:45,760 --> 00:47:48,190 more seriously, and just so that I didn't 1118 00:47:48,190 --> 00:47:49,660 have to feel as different. 1119 00:47:49,660 --> 00:47:51,100 AUDIENCE: Yeah. 1120 00:47:51,100 --> 00:47:55,930 So, yes, like with that, I feel like I've always 1121 00:47:55,930 --> 00:48:01,280 wanted to be who I am, and I don't want to be somebody else. 1122 00:48:01,280 --> 00:48:03,620 So I guess, along those lines, I've 1123 00:48:03,620 --> 00:48:08,210 had one conversation with one of my professors 1124 00:48:08,210 --> 00:48:12,940 where she was explaining that in research, when people were 1125 00:48:12,940 --> 00:48:14,970 giving presentations, they usually 1126 00:48:14,970 --> 00:48:19,320 wore light colors like gray, and white, or black, 1127 00:48:19,320 --> 00:48:22,180 and that you shouldn't be too colorful. 1128 00:48:22,180 --> 00:48:25,180 One time we were going into a conference, 1129 00:48:25,180 --> 00:48:27,130 and I was wearing red. 1130 00:48:27,130 --> 00:48:31,360 I was like really colorful, and then she was like, wow. 1131 00:48:31,360 --> 00:48:33,250 I mean, that's not what we talked about. 1132 00:48:33,250 --> 00:48:34,090 I'm like, yeah. 1133 00:48:34,090 --> 00:48:35,710 Science can be colorful too. 1134 00:48:35,710 --> 00:48:38,770 AUDIENCE: That's great. (LAUGHS) 1135 00:48:38,770 --> 00:48:40,750 AUDIENCE: I want to be who I am. 1136 00:48:40,750 --> 00:48:45,400 And the next minute, I saw a professor, he was so colorful, 1137 00:48:45,400 --> 00:48:47,610 like red and orange all over the place. 1138 00:48:47,610 --> 00:48:48,340 I was like, yes. 1139 00:48:48,340 --> 00:48:49,600 That's what I'm talking about. 1140 00:48:49,600 --> 00:48:50,380 Be who you are. 1141 00:48:50,380 --> 00:48:51,680 Be free. 1142 00:48:51,680 --> 00:48:53,320 And that's how a science should be. 1143 00:48:53,320 --> 00:48:57,133 People should be comfortable in their skin and who they are. 1144 00:48:57,133 --> 00:48:57,758 AUDIENCE: Yeah. 1145 00:48:57,758 --> 00:48:58,383 AUDIENCE: Yeah. 1146 00:48:58,383 --> 00:48:59,590 AUDIENCE: Thank you. 1147 00:48:59,590 --> 00:49:03,220 AUDIENCE: When I'm meeting new scientists, especially 1148 00:49:03,220 --> 00:49:09,640 younger ones, or people who I might have to mentor or train, 1149 00:49:09,640 --> 00:49:11,920 if they ever start to open up about an experience 1150 00:49:11,920 --> 00:49:15,220 where they felt discriminated against or harassed, 1151 00:49:15,220 --> 00:49:17,350 I can't necessarily relate to that experience, 1152 00:49:17,350 --> 00:49:19,660 but I do want to convey that they can trust me, 1153 00:49:19,660 --> 00:49:21,610 and that I'll listen to them, and I'll 1154 00:49:21,610 --> 00:49:23,955 help them in whatever way. 1155 00:49:23,955 --> 00:49:26,080 Because I can't really share a personal experience. 1156 00:49:26,080 --> 00:49:30,580 A lot of times I'll basically turned to relevant literature, 1157 00:49:30,580 --> 00:49:32,020 like the book Whistling Vivaldi. 1158 00:49:32,020 --> 00:49:35,710 And I'll try to communicate, I'm on your side and I'm safe, 1159 00:49:35,710 --> 00:49:39,690 by talking about a subject that communicates to them 1160 00:49:39,690 --> 00:49:41,357 I'm putting effort into this and I care. 1161 00:49:41,357 --> 00:49:42,982 AUDIENCE: Oh, that's a really good one. 1162 00:49:42,982 --> 00:49:44,450 It makes a lot of sense for me. 1163 00:49:44,450 --> 00:49:47,740 It's more just going back again to my identities. 1164 00:49:47,740 --> 00:49:50,135 I have an accent, because I'm from the Caribbean-- 1165 00:49:50,135 --> 00:49:51,760 and I feel like this is common for lots 1166 00:49:51,760 --> 00:49:54,760 of international students in the scientific setting-- 1167 00:49:54,760 --> 00:49:57,850 I definitely try to put on a more, quote, unquote, 1168 00:49:57,850 --> 00:50:01,720 American accent, to ensure that my audience is not 1169 00:50:01,720 --> 00:50:05,080 being distracted by my accent, but really 1170 00:50:05,080 --> 00:50:07,390 focusing on the science, and really trying 1171 00:50:07,390 --> 00:50:10,280 to be more scientific, quote, unquote. 1172 00:50:10,280 --> 00:50:12,490 And the other thing, in terms of physical appearance, 1173 00:50:12,490 --> 00:50:13,740 I have a lot of hair. 1174 00:50:13,740 --> 00:50:15,670 [LAUGHTER] 1175 00:50:15,670 --> 00:50:19,450 It's very contained right now, but it's really big. 1176 00:50:19,450 --> 00:50:22,630 So definitely, in scientific settings, 1177 00:50:22,630 --> 00:50:24,820 I definitely try to pull it back to look more 1178 00:50:24,820 --> 00:50:28,360 what, quote, unquote, would be [INAUDIBLE] kept. 1179 00:50:28,360 --> 00:50:30,850 It's really kind of weird, but yeah, 1180 00:50:30,850 --> 00:50:33,250 so you'll definitely see me either my hair slicked back 1181 00:50:33,250 --> 00:50:36,070 in a scientific setting, versus when you probably will see me 1182 00:50:36,070 --> 00:50:37,420 in lab, or something like that. 1183 00:50:37,420 --> 00:50:39,532 So that's definitely the way I whistle Vivaldi, 1184 00:50:39,532 --> 00:50:40,990 because, again, I don't want people 1185 00:50:40,990 --> 00:50:43,180 to be distracted by my hair, or thinking 1186 00:50:43,180 --> 00:50:44,700 I don't look professional. 1187 00:50:44,700 --> 00:50:46,950 I don't want that to detract from what I'm saying 1188 00:50:46,950 --> 00:50:49,010 and the science that I'm doing. 1189 00:50:49,010 --> 00:50:50,830 So that's definitely another occasion 1190 00:50:50,830 --> 00:50:53,240 when I whistle Vivaldi. 1191 00:50:53,240 --> 00:50:56,700 AUDIENCE: Yeah, that makes a lot of sense. 1192 00:50:56,700 --> 00:50:59,140 AUDIENCE: So I guess for the whistling Vivaldi, 1193 00:50:59,140 --> 00:51:03,465 I did have an example that reminded me of this 1194 00:51:03,465 --> 00:51:04,465 when I was an undergrad. 1195 00:51:04,465 --> 00:51:06,190 AUDIENCE: Mm-hmm. 1196 00:51:06,190 --> 00:51:09,340 AUDIENCE: So I joined a lab my freshman year, 1197 00:51:09,340 --> 00:51:15,500 and I have always been more traditionally feminine. 1198 00:51:15,500 --> 00:51:19,600 My, I don't know, style dressing. 1199 00:51:19,600 --> 00:51:24,160 And I kind of noticed I was one of the only women on the floor, 1200 00:51:24,160 --> 00:51:26,160 and I knew I stood out. 1201 00:51:26,160 --> 00:51:28,080 And I specifically remember that there 1202 00:51:28,080 --> 00:51:32,307 was a professor who asked me what I was doing around 1203 00:51:32,307 --> 00:51:34,890 on the lab floor, and told me I didn't like the research type. 1204 00:51:34,890 --> 00:51:39,720 And that really stuck with me, and after that, I really 1205 00:51:39,720 --> 00:51:40,510 changed. 1206 00:51:40,510 --> 00:51:45,480 I had my lab style and then my everyday Lindsay style. 1207 00:51:45,480 --> 00:51:46,320 AUDIENCE: (LAUGHS) 1208 00:51:46,320 --> 00:51:47,820 AUDIENCE: Whenever I was in the lab, 1209 00:51:47,820 --> 00:51:52,260 I would always wear very baggy clothes, tennis shoes, 1210 00:51:52,260 --> 00:51:53,670 my hair tied back. 1211 00:51:53,670 --> 00:51:57,572 And I felt like my glasses made me look more studious, so I 1212 00:51:57,572 --> 00:51:58,780 would always wear my glasses. 1213 00:52:01,500 --> 00:52:03,390 It's just something I was very conscious of. 1214 00:52:03,390 --> 00:52:04,015 AUDIENCE: Yeah. 1215 00:52:04,015 --> 00:52:07,112 AUDIENCE: I would specifically dressed in one way 1216 00:52:07,112 --> 00:52:09,570 when I was being a scientist, and I would dress another way 1217 00:52:09,570 --> 00:52:11,652 when I was in my outside life. 1218 00:52:11,652 --> 00:52:13,860 AUDIENCE: It's like not easy, because in some setting 1219 00:52:13,860 --> 00:52:15,468 you're judged for being dressed up. 1220 00:52:15,468 --> 00:52:17,760 In other settings you're judged for being dressed down. 1221 00:52:17,760 --> 00:52:18,385 AUDIENCE: Yeah. 1222 00:52:18,385 --> 00:52:19,970 AUDIENCE: Yeah, I totally get that. 1223 00:52:19,970 --> 00:52:24,060 Yeah so, in my case, I think it's more about my race. 1224 00:52:24,060 --> 00:52:27,210 I personally have a stereotype that Asian women that 1225 00:52:27,210 --> 00:52:31,120 are recently immigrated are more reserved, quiet, 1226 00:52:31,120 --> 00:52:33,180 and, I don't know, obedient. 1227 00:52:33,180 --> 00:52:37,230 And although no one actually ever negatively judged 1228 00:52:37,230 --> 00:52:41,640 me for that, I'm constantly afraid to confirm 1229 00:52:41,640 --> 00:52:43,200 that negative stereotype. 1230 00:52:43,200 --> 00:52:46,830 So even if I'm not really that kind of person, 1231 00:52:46,830 --> 00:52:49,980 I try to be very vocal and talkative, especially when I'm 1232 00:52:49,980 --> 00:52:52,620 surrounded by people that I'm meeting for the first time, 1233 00:52:52,620 --> 00:52:54,900 so I'm giving like an impression that I want 1234 00:52:54,900 --> 00:52:57,030 to engage in the conversation. 1235 00:52:57,030 --> 00:53:02,960 Yeah, it was not an easy thing, but I've been doing it. 1236 00:53:02,960 --> 00:53:03,633 AUDIENCE: Yeah. 1237 00:53:03,633 --> 00:53:04,800 CATHY L. DRENNAN: All right. 1238 00:53:04,800 --> 00:53:06,653 So I'm just going to start out. 1239 00:53:06,653 --> 00:53:08,070 I'm going to give you one example. 1240 00:53:08,070 --> 00:53:09,870 This time it's not mine, but I think 1241 00:53:09,870 --> 00:53:14,070 that it was one of the ones that someone suggested 1242 00:53:14,070 --> 00:53:16,000 that really resonated with me. 1243 00:53:16,000 --> 00:53:17,050 So I want to share it. 1244 00:53:17,050 --> 00:53:22,940 So this sense of feeling the need 1245 00:53:22,940 --> 00:53:28,190 to suffer for your science, and that other people 1246 00:53:28,190 --> 00:53:30,320 see someone that are talking about how much they 1247 00:53:30,320 --> 00:53:31,490 suffered for their science. 1248 00:53:31,490 --> 00:53:35,730 And instead of just saying, wait a minute, 1249 00:53:35,730 --> 00:53:37,620 I sleep eight hours a night. 1250 00:53:37,620 --> 00:53:39,780 They're like, yeah I gotta get in on this suffering 1251 00:53:39,780 --> 00:53:41,040 for my science thing. 1252 00:53:41,040 --> 00:53:43,440 So an example, someone was talking 1253 00:53:43,440 --> 00:53:46,560 about how they knew that this person was going 1254 00:53:46,560 --> 00:53:49,410 on this ski trip over the weekend, 1255 00:53:49,410 --> 00:53:52,440 but before they left lab to go on the ski trip, instead 1256 00:53:52,440 --> 00:53:54,780 of saying, hey, see all Monday. 1257 00:53:54,780 --> 00:53:55,980 I'm going skiing. 1258 00:53:55,980 --> 00:53:59,400 They're going out like carrying these journals and papers. 1259 00:53:59,400 --> 00:54:01,440 And they're just like, oh, I got a lot 1260 00:54:01,440 --> 00:54:04,010 of reading to do this weekend. 1261 00:54:04,010 --> 00:54:05,190 Does anyone have a cart? 1262 00:54:05,190 --> 00:54:07,290 I can barely carry it. 1263 00:54:07,290 --> 00:54:11,760 I'm going to be reading all weekend. 1264 00:54:11,760 --> 00:54:13,800 This is a scheme. 1265 00:54:13,800 --> 00:54:14,790 What's going on? 1266 00:54:14,790 --> 00:54:18,360 But it's like, if you're really a scientist, 1267 00:54:18,360 --> 00:54:21,120 if you're a true scientist, if you're a real scientist, 1268 00:54:21,120 --> 00:54:22,500 you suffer. 1269 00:54:22,500 --> 00:54:24,450 You have no hobbies. 1270 00:54:24,450 --> 00:54:28,350 You work day and night in the cold room, in the dark. 1271 00:54:28,350 --> 00:54:33,240 I mean, you're just 36-hour experiments. 1272 00:54:33,240 --> 00:54:34,750 There's no time to shower. 1273 00:54:34,750 --> 00:54:36,660 There's no time to sleep. 1274 00:54:36,660 --> 00:54:39,900 And instead of just saying and sending a message 1275 00:54:39,900 --> 00:54:41,940 to maybe new people in lab, and undergrads, 1276 00:54:41,940 --> 00:54:44,970 and I think this has come up, especially with women, that 1277 00:54:44,970 --> 00:54:48,690 like if you do not give of yourself completely, 1278 00:54:48,690 --> 00:54:50,940 you're not a, quote, real scientist. 1279 00:54:50,940 --> 00:54:54,000 And a lot of people who are thinking about science, 1280 00:54:54,000 --> 00:54:57,330 especially women, think, I'd really like to have a family, 1281 00:54:57,330 --> 00:54:59,830 and I know I might be doing a little more of the caregiving. 1282 00:54:59,830 --> 00:55:01,440 So I don't know if I can do science. 1283 00:55:01,440 --> 00:55:02,857 I might have to do something else. 1284 00:55:02,857 --> 00:55:05,220 Because there's this notion that you 1285 00:55:05,220 --> 00:55:08,370 have to suffer so deeply for your science. 1286 00:55:08,370 --> 00:55:09,850 And people buy into this. 1287 00:55:09,850 --> 00:55:11,670 They don't want to be the one saying, 1288 00:55:11,670 --> 00:55:13,830 I don't know what's wrong with you, but I shower every day. 1289 00:55:13,830 --> 00:55:14,330 Right? 1290 00:55:14,330 --> 00:55:14,850 [LAUGHTER] 1291 00:55:14,850 --> 00:55:16,877 They're just like coming up with something else 1292 00:55:16,877 --> 00:55:17,710 that they're saying. 1293 00:55:17,710 --> 00:55:21,270 It's like, well, yeah, but my experiment was even worse! 1294 00:55:21,270 --> 00:55:24,420 And I had to walk uphill both ways to do it, right? 1295 00:55:24,420 --> 00:55:27,420 Everyone gets into this in the whole lab culture, 1296 00:55:27,420 --> 00:55:29,610 and they're trying to outdo themselves 1297 00:55:29,610 --> 00:55:31,920 with the degree of suffering. 1298 00:55:31,920 --> 00:55:33,730 So that's one example that came up. 1299 00:55:33,730 --> 00:55:36,570 And I do think that these kind of things-- it sounds funny, 1300 00:55:36,570 --> 00:55:38,220 but it's important, because it sends 1301 00:55:38,220 --> 00:55:42,460 a message of what it takes to be a scientist that is not 1302 00:55:42,460 --> 00:55:43,700 really true. 1303 00:55:43,700 --> 00:55:47,170 And if we have well-adjusted human beings doing science, 1304 00:55:47,170 --> 00:55:48,250 I'm OK for that. 1305 00:55:48,250 --> 00:55:51,340 I think that's a good thing to have well-adjusted human beings 1306 00:55:51,340 --> 00:55:52,240 doing science. 1307 00:55:52,240 --> 00:55:57,400 So we have to think a little bit about fitting into a culture, 1308 00:55:57,400 --> 00:56:00,360 and what that is, and what we do to fit in. 1309 00:56:00,360 --> 00:56:00,860 All right. 1310 00:56:00,860 --> 00:56:03,880 So that's just one example that was shared with me 1311 00:56:03,880 --> 00:56:05,930 before, which I thought was a good one. 1312 00:56:05,930 --> 00:56:08,950 But it seems like you guys were having some lively discussion. 1313 00:56:08,950 --> 00:56:11,980 So who would like to start off by sharing 1314 00:56:11,980 --> 00:56:14,820 what you were talking about? 1315 00:56:14,820 --> 00:56:15,320 Yeah? 1316 00:56:15,320 --> 00:56:18,790 AUDIENCE: Yeah, so depending on who I'm talking with, 1317 00:56:18,790 --> 00:56:21,340 I actually tune my accent. 1318 00:56:21,340 --> 00:56:23,680 For example, when I'm back home, I intentionally 1319 00:56:23,680 --> 00:56:27,580 mispronounce certain words so that I can talk with my friends 1320 00:56:27,580 --> 00:56:28,930 and family. 1321 00:56:28,930 --> 00:56:31,510 And then whenever I'm here in an academic setting, 1322 00:56:31,510 --> 00:56:34,630 I try to pronounce everything correctly. 1323 00:56:34,630 --> 00:56:35,420 Yeah. 1324 00:56:35,420 --> 00:56:37,630 CATHY L. DRENNAN: Yeah, so the sort of, I'm 1325 00:56:37,630 --> 00:56:38,950 still sort of one of you. 1326 00:56:38,950 --> 00:56:42,580 I've gone off to this place of MIT, 1327 00:56:42,580 --> 00:56:45,700 but I'm back having these conversations. 1328 00:56:45,700 --> 00:56:49,250 And then you present a different side when you're here. 1329 00:56:49,250 --> 00:56:55,090 I've heard that a lot, that sometimes being at a top place, 1330 00:56:55,090 --> 00:56:58,060 people feeling uncomfortable going home, 1331 00:56:58,060 --> 00:57:01,360 that the family members just feel like they 1332 00:57:01,360 --> 00:57:03,027 don't want to sound stupid, so they just 1333 00:57:03,027 --> 00:57:04,610 don't know what to say to you anymore. 1334 00:57:04,610 --> 00:57:05,730 It's like, it's still me. 1335 00:57:05,730 --> 00:57:09,070 It's OK. (LAUGHS) That's great. 1336 00:57:09,070 --> 00:57:11,110 OK. 1337 00:57:11,110 --> 00:57:11,830 Anybody else? 1338 00:57:11,830 --> 00:57:12,430 Yeah, yeah? 1339 00:57:12,430 --> 00:57:15,130 AUDIENCE: Yeah, so I personally have 1340 00:57:15,130 --> 00:57:17,622 a stereotype that Asian women, especially those who are, 1341 00:57:17,622 --> 00:57:19,080 quote, unquote, fresh off the boat, 1342 00:57:19,080 --> 00:57:22,420 are more reserved and quiet. 1343 00:57:22,420 --> 00:57:27,160 And while I'm a person who kind of confirms that stereotype, 1344 00:57:27,160 --> 00:57:30,520 when I'm with people that I'm meeting for the first time, 1345 00:57:30,520 --> 00:57:33,300 I try to be a little more vocal and talkative, 1346 00:57:33,300 --> 00:57:35,260 to give an impression that I want 1347 00:57:35,260 --> 00:57:37,010 to engage in this conversation. 1348 00:57:37,010 --> 00:57:38,620 And sometimes that makes you feel bad, 1349 00:57:38,620 --> 00:57:40,060 because I'm not true to myself. 1350 00:57:40,060 --> 00:57:41,060 So, yeah. 1351 00:57:41,060 --> 00:57:42,610 CATHY L. DRENNAN: Yeah, yeah. 1352 00:57:42,610 --> 00:57:44,240 People may not believe this about me, 1353 00:57:44,240 --> 00:57:45,460 but I used to be really shy. 1354 00:57:45,460 --> 00:57:47,680 [SOFT LAUGHTER] 1355 00:57:47,680 --> 00:57:48,520 So, yeah. 1356 00:57:48,520 --> 00:57:52,220 So all those years of imposter syndrome-- 1357 00:57:52,220 --> 00:57:52,810 all right. 1358 00:57:52,810 --> 00:57:54,940 I'm going to wear my not-shy clothes, 1359 00:57:54,940 --> 00:57:56,800 and talk like a not-shy person. 1360 00:57:56,800 --> 00:57:58,690 But yeah, I had to really struggle with that, 1361 00:57:58,690 --> 00:58:01,180 and I think it's very hard. 1362 00:58:01,180 --> 00:58:04,100 When you are worried about that stereotype, 1363 00:58:04,100 --> 00:58:05,370 it's pushing yourself out. 1364 00:58:05,370 --> 00:58:06,820 Being a little more-- 1365 00:58:06,820 --> 00:58:10,360 so that's it that's a really, really nice example. 1366 00:58:10,360 --> 00:58:11,910 Anyone else? 1367 00:58:11,910 --> 00:58:12,550 Yeah? 1368 00:58:12,550 --> 00:58:14,560 AUDIENCE: Yeah, so I have several tattoos, 1369 00:58:14,560 --> 00:58:16,030 and I also have a nose piercing. 1370 00:58:16,030 --> 00:58:18,050 And when I'm in professional settings, 1371 00:58:18,050 --> 00:58:21,580 especially at conferences, I try to cover up my tattoos, 1372 00:58:21,580 --> 00:58:24,190 and sometimes they even put in a different nose 1373 00:58:24,190 --> 00:58:26,800 ring that's clear so you really can't see it 1374 00:58:26,800 --> 00:58:28,180 unless you're close up. 1375 00:58:28,180 --> 00:58:31,480 And I try to do that in order to not confirm 1376 00:58:31,480 --> 00:58:34,060 a negative stereotype of people who decide to put art 1377 00:58:34,060 --> 00:58:37,300 on their body or have extra piercings, 1378 00:58:37,300 --> 00:58:40,840 and to appear more professional, and that I understand 1379 00:58:40,840 --> 00:58:42,305 and know what I'm talking about. 1380 00:58:42,305 --> 00:58:44,680 CATHY L. DRENNAN: Yeah, that's also a really-- everyone's 1381 00:58:44,680 --> 00:58:47,680 got really great examples. 1382 00:58:47,680 --> 00:58:50,190 That has kind of come up in [INAUDIBLE] really thinking 1383 00:58:50,190 --> 00:58:51,940 about how you're dressed and other things. 1384 00:58:51,940 --> 00:58:56,020 And one time I had videotapes to use 1385 00:58:56,020 --> 00:58:58,360 in freshman chemistry, of scientists talking about how 1386 00:58:58,360 --> 00:58:59,645 they were using chemistry. 1387 00:58:59,645 --> 00:59:01,270 And I got all this feedback because one 1388 00:59:01,270 --> 00:59:04,330 of the people in the video had piercings and tattoos. 1389 00:59:04,330 --> 00:59:07,690 And they're like, but that's not a scientist, right? 1390 00:59:07,690 --> 00:59:09,610 But then he talked so articulately, 1391 00:59:09,610 --> 00:59:11,260 and it was mind-blowing. 1392 00:59:11,260 --> 00:59:13,210 But we do have these ideas of what 1393 00:59:13,210 --> 00:59:15,130 scientists should look like. 1394 00:59:15,130 --> 00:59:17,020 I partly blame Hollywood. 1395 00:59:17,020 --> 00:59:20,800 Polyester pants, and the lab coat, and pocket protectors. 1396 00:59:20,800 --> 00:59:24,410 And it turns out scientists look like everyone else. 1397 00:59:24,410 --> 00:59:26,470 But then we worry, and sometimes try 1398 00:59:26,470 --> 00:59:31,030 to fit in, and whistle a little Vivaldi to hide maybe 1399 00:59:31,030 --> 00:59:31,990 some parts of that. 1400 00:59:31,990 --> 00:59:33,340 And so, yeah. 1401 00:59:33,340 --> 00:59:35,350 Really, really good examples everyone. 1402 00:59:35,350 --> 00:59:35,980 OK. 1403 00:59:35,980 --> 00:59:37,780 Let's move on. 1404 00:59:37,780 --> 00:59:39,760 Who has stereotypes? 1405 00:59:39,760 --> 00:59:41,112 ENTIRE AUDIENCE: Everyone! 1406 00:59:41,112 --> 00:59:42,070 CATHY L. DRENNAN: Yeah. 1407 00:59:42,070 --> 00:59:43,000 Sorry. 1408 00:59:43,000 --> 00:59:44,790 Sorry for the bad news. 1409 00:59:44,790 --> 00:59:47,440 But let me show you some data in case you're not convinced. 1410 00:59:47,440 --> 00:59:47,940 All right. 1411 00:59:47,940 --> 00:59:50,200 So there's a whole bunch of studies, 1412 00:59:50,200 --> 00:59:52,030 and I'm just going to tell you about a few 1413 00:59:52,030 --> 00:59:56,410 of them that get at this fact that we all 1414 00:59:56,410 --> 00:59:57,670 do have stereotypes. 1415 00:59:57,670 --> 01:00:00,490 Because we all live in a culture, we're all part of it. 1416 01:00:00,490 --> 01:00:03,160 We are, in fact, not in the cold room for 36 hours. 1417 01:00:03,160 --> 01:00:07,250 We come out to socialize with other people at some point, 1418 01:00:07,250 --> 01:00:10,730 and so we develop stereotypes. 1419 01:00:10,730 --> 01:00:11,230 All right. 1420 01:00:11,230 --> 01:00:13,450 So there are these a bunch of different studies 1421 01:00:13,450 --> 01:00:17,200 where they show groups, multiple different CVs. 1422 01:00:17,200 --> 01:00:19,510 And they have one CV that's clearly 1423 01:00:19,510 --> 01:00:22,930 a man's name, another CV that's clearly a woman's name, 1424 01:00:22,930 --> 01:00:25,330 and then they ask this group, pick the best 1425 01:00:25,330 --> 01:00:26,830 candidate for something. 1426 01:00:26,830 --> 01:00:29,060 And the data show that if the see 1427 01:00:29,060 --> 01:00:31,720 these are designed to be very, very similar-- 1428 01:00:31,720 --> 01:00:34,180 similar kinds of schools, similar numbers 1429 01:00:34,180 --> 01:00:36,550 of publications, similar awards-- 1430 01:00:36,550 --> 01:00:40,270 that people will pick the CV with the man's name 1431 01:00:40,270 --> 01:00:41,710 on as being better. 1432 01:00:41,710 --> 01:00:44,800 And importantly, women do this too. 1433 01:00:44,800 --> 01:00:47,980 So this idea of, let's put a woman on every committee 1434 01:00:47,980 --> 01:00:52,120 so that we make sure that this committee is doing the best, 1435 01:00:52,120 --> 01:00:54,610 picking the best candidate, whatever, women 1436 01:00:54,610 --> 01:00:56,370 do the exact same thing as men. 1437 01:00:56,370 --> 01:00:58,570 They also pick the male candidate 1438 01:00:58,570 --> 01:01:01,240 when the CVs are very similar. 1439 01:01:01,240 --> 01:01:06,310 So a lot of these studies were done in the English profession, 1440 01:01:06,310 --> 01:01:10,180 but Joe Handelsman and a bunch of others in this publication, 1441 01:01:10,180 --> 01:01:16,030 in PNAS in 2012, redid did this CV study, 1442 01:01:16,030 --> 01:01:18,130 but it was only for scientists. 1443 01:01:18,130 --> 01:01:22,270 So people would say, scientists are logical, right? 1444 01:01:22,270 --> 01:01:24,160 We can't have stereotypes. 1445 01:01:24,160 --> 01:01:25,570 We're about facts. 1446 01:01:25,570 --> 01:01:26,350 We're about data. 1447 01:01:26,350 --> 01:01:29,860 We are so logical we can't possibly 1448 01:01:29,860 --> 01:01:31,330 really have stereotypes. 1449 01:01:31,330 --> 01:01:33,550 And so, clear convincing evidence 1450 01:01:33,550 --> 01:01:35,380 that that is, in fact, not true. 1451 01:01:35,380 --> 01:01:37,660 Scientists are just like all other human beings, 1452 01:01:37,660 --> 01:01:38,600 as it turns out. 1453 01:01:38,600 --> 01:01:40,730 They're people. 1454 01:01:40,730 --> 01:01:42,800 Anyhow, so this study. 1455 01:01:42,800 --> 01:01:43,580 Loved this study. 1456 01:01:43,580 --> 01:01:44,540 Jealous of this study. 1457 01:01:44,540 --> 01:01:46,860 Why did I not do this study? 1458 01:01:46,860 --> 01:01:48,410 Anyhow. 1459 01:01:48,410 --> 01:01:52,010 So what they did is they took this resume, CV, said it 1460 01:01:52,010 --> 01:01:54,140 was for a lab manager position. 1461 01:01:54,140 --> 01:01:56,840 They had the name John on it or the name Jennifer, 1462 01:01:56,840 --> 01:01:58,460 and they sent it to different people, 1463 01:01:58,460 --> 01:02:01,820 and then saw what people thought collectively about John, 1464 01:02:01,820 --> 01:02:05,120 and what people thought collectively about Jennifer. 1465 01:02:05,120 --> 01:02:09,170 Exactly the same CV, so they should 1466 01:02:09,170 --> 01:02:11,960 have thought the same things about both of them, 1467 01:02:11,960 --> 01:02:15,170 because it was the same. 1468 01:02:15,170 --> 01:02:20,690 But then they compared for the male name versus female 1469 01:02:20,690 --> 01:02:23,390 who's applying for this lab manager position, 1470 01:02:23,390 --> 01:02:27,010 they thought that the man was more competent, 1471 01:02:27,010 --> 01:02:29,920 that the man was more hireable, and was 1472 01:02:29,920 --> 01:02:32,470 going to be easier to mentor, and also 1473 01:02:32,470 --> 01:02:34,480 should get pay to lot more. 1474 01:02:34,480 --> 01:02:38,230 Now, again, let me emphasize, these are identical documents, 1475 01:02:38,230 --> 01:02:40,120 except one has the name John on it, 1476 01:02:40,120 --> 01:02:42,440 and one had the name Jennifer on it. 1477 01:02:42,440 --> 01:02:46,300 And they looked at how men viewed this and how women did. 1478 01:02:46,300 --> 01:02:49,330 It was male faculty, women faculty, again, 1479 01:02:49,330 --> 01:02:50,980 for a lab manager position. 1480 01:02:50,980 --> 01:02:52,780 Women thought the same thing. 1481 01:02:52,780 --> 01:02:55,330 Women thought that the CV with the name John on it 1482 01:02:55,330 --> 01:02:58,990 was a lot more impressive than the CV with the name Jennifer. 1483 01:02:58,990 --> 01:03:03,580 So scientists, the ivory tower, is just 1484 01:03:03,580 --> 01:03:06,525 locked-in people with the same prejudices 1485 01:03:06,525 --> 01:03:07,650 that you find other places. 1486 01:03:07,650 --> 01:03:09,460 The same stereotypes. 1487 01:03:09,460 --> 01:03:10,350 All right. 1488 01:03:10,350 --> 01:03:10,850 OK. 1489 01:03:10,850 --> 01:03:15,010 So we need some good news. 1490 01:03:15,010 --> 01:03:16,690 I think it's time for good news. 1491 01:03:16,690 --> 01:03:18,820 I've convinced you that you can all 1492 01:03:18,820 --> 01:03:23,740 be victims of stereotype threat, that you all have stereotypes. 1493 01:03:23,740 --> 01:03:27,040 Probably, you're all worried about imposter syndrome. 1494 01:03:27,040 --> 01:03:30,733 If you didn't have it before, I'm sure you do now. 1495 01:03:30,733 --> 01:03:31,900 But you know that I have it. 1496 01:03:31,900 --> 01:03:34,970 Maybe that means like-- 1497 01:03:34,970 --> 01:03:36,230 anyway. 1498 01:03:36,230 --> 01:03:38,230 Good news. 1499 01:03:38,230 --> 01:03:40,270 Wise criticism. 1500 01:03:40,270 --> 01:03:42,960 So this can be used to mitigate the effects of stereotype 1501 01:03:42,960 --> 01:03:43,590 threat. 1502 01:03:43,590 --> 01:03:46,950 And again, it's criticism that's delivered in such a way 1503 01:03:46,950 --> 01:03:50,970 that students feel like they're capable of a high level 1504 01:03:50,970 --> 01:03:54,390 of achievement, that students are less defensive 1505 01:03:54,390 --> 01:03:56,500 and feel less threatened. 1506 01:03:56,500 --> 01:04:03,510 And so, here, again, it's not about whether there's criticism 1507 01:04:03,510 --> 01:04:08,310 or not, it's really about how that criticism is delivered. 1508 01:04:08,310 --> 01:04:10,710 So I'll just give you a brief example 1509 01:04:10,710 --> 01:04:12,150 what I think about here. 1510 01:04:12,150 --> 01:04:15,080 When I started as Assistant Professor, 1511 01:04:15,080 --> 01:04:17,520 it was the first time I was writing a paper from my lab 1512 01:04:17,520 --> 01:04:21,570 that my advisor's not deciding it's OK or not. 1513 01:04:21,570 --> 01:04:22,740 This is for me. 1514 01:04:22,740 --> 01:04:24,900 So I gave it to one of my senior colleagues 1515 01:04:24,900 --> 01:04:27,630 to critique before I sent it out. 1516 01:04:27,630 --> 01:04:33,360 It came back covered in red pen. 1517 01:04:33,360 --> 01:04:37,810 [INAUDIBLE] And I was just thinking, what was I thinking? 1518 01:04:37,810 --> 01:04:38,980 I can't be a professor. 1519 01:04:38,980 --> 01:04:40,540 This is the most ridiculous thing. 1520 01:04:40,540 --> 01:04:44,570 How did I ever think that I could possibly do this? 1521 01:04:44,570 --> 01:04:47,080 But then I got a little perspective on it. 1522 01:04:47,080 --> 01:04:51,790 I realized that this professor had taken time 1523 01:04:51,790 --> 01:04:56,560 to not only read the paper through and make comments, 1524 01:04:56,560 --> 01:05:00,430 but there were notes about reading the background 1525 01:05:00,430 --> 01:05:02,310 literature on the paper. 1526 01:05:02,310 --> 01:05:08,050 This wasn't their field, so they had read all the references 1527 01:05:08,050 --> 01:05:11,140 and thought about how I could have improved the paper. 1528 01:05:11,140 --> 01:05:13,450 Who has time to do that? 1529 01:05:13,450 --> 01:05:15,460 The only person who has time to do that 1530 01:05:15,460 --> 01:05:19,660 is someone who is completely and totally dedicated 1531 01:05:19,660 --> 01:05:22,160 to my success. 1532 01:05:22,160 --> 01:05:24,160 So it was criticism. 1533 01:05:24,160 --> 01:05:25,420 It was criticism. 1534 01:05:25,420 --> 01:05:26,260 There was criticism! 1535 01:05:26,260 --> 01:05:28,360 [LAUGHTER] 1536 01:05:28,360 --> 01:05:31,900 But it was criticism that was delivered because they 1537 01:05:31,900 --> 01:05:34,360 believed I could do this, and wanted 1538 01:05:34,360 --> 01:05:38,290 to help me, put their money where their mouth was, 1539 01:05:38,290 --> 01:05:41,380 spend time helping me be successful. 1540 01:05:41,380 --> 01:05:44,860 So next time you get back a draft covered with red, 1541 01:05:44,860 --> 01:05:49,090 don't think of it as harsh, I can't do it, 1542 01:05:49,090 --> 01:05:52,490 blood of a massacre of the paper spewing out. 1543 01:05:52,490 --> 01:05:54,490 You think about it as love. 1544 01:05:54,490 --> 01:05:57,160 [LAUGHTER] 1545 01:05:57,160 --> 01:05:58,450 Criticism takes time. 1546 01:05:58,450 --> 01:05:59,920 It's love! 1547 01:05:59,920 --> 01:06:05,410 Red love pouring out all over the paper. 1548 01:06:05,410 --> 01:06:07,600 Criticism is important, but the way 1549 01:06:07,600 --> 01:06:10,210 it's delivered-- if you feel it's delivered in a way 1550 01:06:10,210 --> 01:06:13,270 that the person really believes in you, 1551 01:06:13,270 --> 01:06:16,990 then it can really motivate you to do better. 1552 01:06:16,990 --> 01:06:19,810 So criticism is good, but how you deliver 1553 01:06:19,810 --> 01:06:21,260 it makes a huge difference. 1554 01:06:21,260 --> 01:06:21,760 All right. 1555 01:06:21,760 --> 01:06:25,150 So Mary O'Reilly who is a graduate student here at MIT, 1556 01:06:25,150 --> 01:06:27,550 did all these really cool little cartoons. 1557 01:06:27,550 --> 01:06:30,900 Little call out to Mary, now. 1558 01:06:30,900 --> 01:06:33,467 You're a student now, but think about what you can do. 1559 01:06:33,467 --> 01:06:35,800 You don't have to sacrifice completely for your science. 1560 01:06:35,800 --> 01:06:38,200 You can do art on the side. 1561 01:06:38,200 --> 01:06:41,410 So Mary had a hobby and it's put to good use. 1562 01:06:41,410 --> 01:06:44,220 So which response would you rather hear 1563 01:06:44,220 --> 01:06:45,780 if you did poorly on a test? 1564 01:06:45,780 --> 01:06:47,340 And I don't know if you can see this. 1565 01:06:47,340 --> 01:06:51,000 This is a C-, which is not a good score for someone who is 1566 01:06:51,000 --> 01:06:54,090 a high achiever. 1567 01:06:54,090 --> 01:06:56,910 Undeserved praise can be super harmful. 1568 01:06:56,910 --> 01:06:59,290 "This is a good score for you. 1569 01:06:59,290 --> 01:07:02,010 You should be pleased." 1570 01:07:02,010 --> 01:07:03,840 What is this person telling this person 1571 01:07:03,840 --> 01:07:05,805 by giving them that praise? 1572 01:07:08,933 --> 01:07:10,100 That's the best they can do. 1573 01:07:10,100 --> 01:07:12,460 You should be happy with a C-. 1574 01:07:12,460 --> 01:07:12,960 No. 1575 01:07:12,960 --> 01:07:16,163 No one should be happy with a C-. 1576 01:07:16,163 --> 01:07:18,080 Of course, if it's pass, no record, and you're 1577 01:07:18,080 --> 01:07:20,270 just shooting for that pass-- but, no, no, no. 1578 01:07:20,270 --> 01:07:21,680 No one should be happy with that. 1579 01:07:21,680 --> 01:07:21,920 All right. 1580 01:07:21,920 --> 01:07:23,462 So you have to be careful about that. 1581 01:07:23,462 --> 01:07:26,390 The worst thing you can do is send the message to someone 1582 01:07:26,390 --> 01:07:29,275 that they messed up, but that's all that it can be expected 1583 01:07:29,275 --> 01:07:32,180 of you is to mess up. 1584 01:07:32,180 --> 01:07:33,170 Bad criticism. 1585 01:07:33,170 --> 01:07:34,790 We may have all received that. 1586 01:07:34,790 --> 01:07:38,480 Yeah, you'll have to try harder if you want to pass. 1587 01:07:38,480 --> 01:07:41,630 No feedback can also be harmful, because you 1588 01:07:41,630 --> 01:07:44,180 might have this idea that you don't belong here. 1589 01:07:44,180 --> 01:07:47,480 And if no one's countering that and telling you you do belong, 1590 01:07:47,480 --> 01:07:50,720 then you're just taking that message away. 1591 01:07:50,720 --> 01:07:55,010 And wise criticism can be, again, the most powerful thing. 1592 01:07:55,010 --> 01:07:57,710 Saying, hey, I know you were doing really 1593 01:07:57,710 --> 01:07:58,790 well in recitation. 1594 01:07:58,790 --> 01:08:00,440 I'm not sure what happened on the exam, 1595 01:08:00,440 --> 01:08:02,930 but let's sit together and try to figure out. 1596 01:08:02,930 --> 01:08:04,790 You did this part really well. 1597 01:08:04,790 --> 01:08:07,370 You just need to figure out how to demonstrate your knowledge 1598 01:08:07,370 --> 01:08:09,540 on this other part of the exam. 1599 01:08:09,540 --> 01:08:12,980 So just a brief story on this. 1600 01:08:12,980 --> 01:08:15,110 Some of my friends who are professors, parents 1601 01:08:15,110 --> 01:08:15,933 call them up. 1602 01:08:15,933 --> 01:08:17,600 Do you know how much tuition I'm paying? 1603 01:08:17,600 --> 01:08:19,410 Why did my kid not get an A? 1604 01:08:19,410 --> 01:08:21,439 I have never gotten that phone call here, 1605 01:08:21,439 --> 01:08:24,810 partly because everybody knows that MIT is a tough place, 1606 01:08:24,810 --> 01:08:27,380 and so, they don't send their kid with the idea 1607 01:08:27,380 --> 01:08:31,550 that the money in tuition is guaranteeing any kind of grade. 1608 01:08:31,550 --> 01:08:34,100 But I did get a phone call from a parent once. 1609 01:08:34,100 --> 01:08:36,109 And they called me and they said, 1610 01:08:36,109 --> 01:08:38,010 I just had to share with you this story, 1611 01:08:38,010 --> 01:08:40,100 it's about my daughter who's in your class. 1612 01:08:40,100 --> 01:08:43,460 They said, first exam comes in MIT, 1613 01:08:43,460 --> 01:08:45,319 and she doesn't do that well. 1614 01:08:45,319 --> 01:08:50,850 And she calls me, and she's crying, and she's crying a lot. 1615 01:08:50,850 --> 01:08:52,680 And she said, dad, I really studied. 1616 01:08:52,680 --> 01:08:54,460 I'm telling you, I really studied. 1617 01:08:54,460 --> 01:08:57,810 I thought I knew this material, but this was my grade. 1618 01:08:57,810 --> 01:09:01,069 And I don't know what happened, I really thought I knew it. 1619 01:09:01,069 --> 01:09:03,300 And she's like, can you come and get me? 1620 01:09:03,300 --> 01:09:05,330 He's like, the first exam. 1621 01:09:05,330 --> 01:09:07,740 It's still September, right? 1622 01:09:07,740 --> 01:09:09,410 And so, he doesn't know what to do, 1623 01:09:09,410 --> 01:09:14,380 and he says, well, I'm going to come get you immediately. 1624 01:09:14,380 --> 01:09:18,330 Let's just see how you feel in a little bit. 1625 01:09:18,330 --> 01:09:20,300 So then he gets a call back a few hours later. 1626 01:09:20,300 --> 01:09:21,700 And you say, Dad! 1627 01:09:21,700 --> 01:09:22,370 Dad! 1628 01:09:22,370 --> 01:09:22,970 Dad! 1629 01:09:22,970 --> 01:09:25,399 My TA emailed me! 1630 01:09:25,399 --> 01:09:28,790 They said, I know you know the material better 1631 01:09:28,790 --> 01:09:30,470 than your grade on the test. 1632 01:09:30,470 --> 01:09:31,670 I told you, dad! 1633 01:09:31,670 --> 01:09:34,279 I told you that I knew the material better. 1634 01:09:34,279 --> 01:09:35,840 And the TA knew I knew! 1635 01:09:35,840 --> 01:09:37,520 I knew I knew the material better! 1636 01:09:37,520 --> 01:09:39,050 He said, you can come in and talk 1637 01:09:39,050 --> 01:09:40,772 about test-taking strategies. 1638 01:09:40,772 --> 01:09:43,189 And so, the dad said, so I don't need to come and get you? 1639 01:09:43,189 --> 01:09:44,590 She's like, no, I'm good. 1640 01:09:44,590 --> 01:09:45,500 Right? 1641 01:09:45,500 --> 01:09:52,520 So a little bit of the message that that TA sent, 1642 01:09:52,520 --> 01:09:54,350 saying, I know that you know this better. 1643 01:09:54,350 --> 01:09:56,480 Let's get together and figure out what we can do. 1644 01:09:56,480 --> 01:10:00,030 It was everything to this one person. 1645 01:10:00,030 --> 01:10:02,860 So wise criticism, very helpful. 1646 01:10:02,860 --> 01:10:06,210 He still said, yeah, that wasn't a good performance, but I 1647 01:10:06,210 --> 01:10:07,480 you can do better. 1648 01:10:07,480 --> 01:10:08,550 OK. 1649 01:10:08,550 --> 01:10:12,467 So I just want to emphasize that no feedback is not a solution. 1650 01:10:12,467 --> 01:10:14,550 Sometimes having these conversations of criticism, 1651 01:10:14,550 --> 01:10:16,210 ugh. 1652 01:10:16,210 --> 01:10:18,810 So the easiest route is say nothing. 1653 01:10:18,810 --> 01:10:20,390 But that is not a good route. 1654 01:10:20,390 --> 01:10:22,680 And let me just try to convince you of that. 1655 01:10:22,680 --> 01:10:27,120 In doing so, we have some more cool Mary cartoons. 1656 01:10:27,120 --> 01:10:29,550 And we're just going to leave the classroom for a minute. 1657 01:10:29,550 --> 01:10:31,710 In this scenario, we have a professor 1658 01:10:31,710 --> 01:10:35,100 coming in, yelling at this male student, 1659 01:10:35,100 --> 01:10:37,080 and saying nothing to this female student who 1660 01:10:37,080 --> 01:10:38,700 is sitting right next to him. 1661 01:10:38,700 --> 01:10:40,642 So the professor comes in, said, you should 1662 01:10:40,642 --> 01:10:41,850 have had this done yesterday! 1663 01:10:41,850 --> 01:10:43,620 I'm giving a talk next week in Texas, 1664 01:10:43,620 --> 01:10:46,292 and I need to have these PowerPoint slides! 1665 01:10:46,292 --> 01:10:48,000 Why haven't you finished this experiment? 1666 01:10:48,000 --> 01:10:49,200 I have to get going! 1667 01:10:49,200 --> 01:10:50,950 And then he leaves the lab. 1668 01:10:50,950 --> 01:10:54,280 And so, in the little blurb here from the female students 1669 01:10:54,280 --> 01:10:56,410 thoughts he can't hear, she said, 1670 01:10:56,410 --> 01:11:00,070 I wish she cared as much about my project. 1671 01:11:00,070 --> 01:11:02,550 It's just not fair. 1672 01:11:02,550 --> 01:11:04,830 And then the man is thinking, he never 1673 01:11:04,830 --> 01:11:07,020 yells at the girls like that, and her project 1674 01:11:07,020 --> 01:11:08,760 isn't going any better than mine. 1675 01:11:08,760 --> 01:11:10,620 It's just not fair. 1676 01:11:10,620 --> 01:11:14,970 So the woman is upset about not getting yelled at, 1677 01:11:14,970 --> 01:11:17,070 which sort of seems counterintuitive at first, 1678 01:11:17,070 --> 01:11:20,430 until you realize that the one graduate 1679 01:11:20,430 --> 01:11:22,020 student's getting yelled at because he 1680 01:11:22,020 --> 01:11:25,110 wants his data for a talk. 1681 01:11:25,110 --> 01:11:27,600 So he's never asking the female student for her data 1682 01:11:27,600 --> 01:11:28,440 for a talk. 1683 01:11:28,440 --> 01:11:30,960 He's never really talking to her at all about her research. 1684 01:11:30,960 --> 01:11:34,320 It's not going well, and he doesn't even care. 1685 01:11:34,320 --> 01:11:37,560 She is apparently so insignificant to him. 1686 01:11:37,560 --> 01:11:40,560 She's too insignificant, her research is so unimportant, 1687 01:11:40,560 --> 01:11:42,552 he doesn't even have time to yell at her. 1688 01:11:42,552 --> 01:11:44,010 He's not interest in yelling at her 1689 01:11:44,010 --> 01:11:46,343 because he's not going to use her research for anything. 1690 01:11:46,343 --> 01:11:50,130 He has no expectations that she would ever deliver something 1691 01:11:50,130 --> 01:11:52,050 that would be talk worthy. 1692 01:11:52,050 --> 01:11:54,000 That's at least what's in her mind. 1693 01:11:54,000 --> 01:11:56,250 Meanwhile, the male professor's very proud of himself 1694 01:11:56,250 --> 01:11:58,260 for never yelling at the girls. 1695 01:11:58,260 --> 01:12:00,930 And the male students are really angry that the girls don't 1696 01:12:00,930 --> 01:12:03,630 get yelled at, not realizing that he's 1697 01:12:03,630 --> 01:12:04,840 paying some attention. 1698 01:12:04,840 --> 01:12:07,090 He actually cares if they're getting results. 1699 01:12:07,090 --> 01:12:12,480 So this is a lose-lose situation for everybody. 1700 01:12:12,480 --> 01:12:17,040 So along these lines I was called on to, at one point, 1701 01:12:17,040 --> 01:12:19,590 interview all of the female graduate students 1702 01:12:19,590 --> 01:12:21,840 in the chemistry department, and all the male students 1703 01:12:21,840 --> 01:12:23,640 in the chemistry department, to figure out 1704 01:12:23,640 --> 01:12:25,265 how they figured out whether they would 1705 01:12:25,265 --> 01:12:27,180 apply for academic positions. 1706 01:12:27,180 --> 01:12:31,620 And I was given this task because the department was 1707 01:12:31,620 --> 01:12:34,950 very low on the list of the number of female graduate 1708 01:12:34,950 --> 01:12:37,250 students who were applying for faculty positions. 1709 01:12:37,250 --> 01:12:40,470 So they wanted to figure out why the women weren't applying. 1710 01:12:40,470 --> 01:12:44,320 And who better to figure that out than me, a woman. 1711 01:12:44,320 --> 01:12:44,820 All right. 1712 01:12:44,820 --> 01:12:47,070 So I actually was kind of curious 1713 01:12:47,070 --> 01:12:48,510 to see what people would say. 1714 01:12:48,510 --> 01:12:51,540 So again, this is not statistically significant. 1715 01:12:51,540 --> 01:12:53,910 I've collected some data over the years that 1716 01:12:53,910 --> 01:12:55,585 is statistically significant. 1717 01:12:55,585 --> 01:12:57,210 This was just conversations that I had, 1718 01:12:57,210 --> 01:12:59,070 but I just wanted to share this with you. 1719 01:12:59,070 --> 01:13:00,930 So I asked both men and women, how 1720 01:13:00,930 --> 01:13:03,940 do you know if you're good enough to be a professor? 1721 01:13:03,940 --> 01:13:07,380 And both said, I don't know. 1722 01:13:07,380 --> 01:13:08,110 It was the same. 1723 01:13:08,110 --> 01:13:09,420 It was the same answer. 1724 01:13:09,420 --> 01:13:10,990 They both said, I don't know. 1725 01:13:10,990 --> 01:13:13,080 And it's like, well, what do you do with that? 1726 01:13:13,080 --> 01:13:16,590 And the women said, well, I wait for the professor to tell me 1727 01:13:16,590 --> 01:13:17,670 that I'm good enough. 1728 01:13:17,670 --> 01:13:20,750 And if they don't tell me, I assume I'm not. 1729 01:13:20,750 --> 01:13:24,680 The men said, well, I go ahead and apply, 1730 01:13:24,680 --> 01:13:26,590 and if I'm not good enough, the professor 1731 01:13:26,590 --> 01:13:29,250 is likely to tell me that at some point. 1732 01:13:29,250 --> 01:13:33,140 So meanwhile, the professor is saying nothing 1733 01:13:33,140 --> 01:13:35,660 to either group about whether they're good enough. 1734 01:13:35,660 --> 01:13:38,480 The women are taking that as a message they're not. 1735 01:13:38,480 --> 01:13:40,020 The men are saying, I'm just going 1736 01:13:40,020 --> 01:13:42,920 to go ahead and try until someone stops me. 1737 01:13:42,920 --> 01:13:46,370 So no feedback, you might be sending 1738 01:13:46,370 --> 01:13:47,900 messages you don't intend. 1739 01:13:47,900 --> 01:13:51,350 I don't think these professors were intending 1740 01:13:51,350 --> 01:13:53,330 to send message to the female students 1741 01:13:53,330 --> 01:13:54,710 that they weren't good enough. 1742 01:13:54,710 --> 01:13:57,650 They just were busy doing stuff and didn't say anything 1743 01:13:57,650 --> 01:13:58,490 to anyone. 1744 01:13:58,490 --> 01:14:02,060 So it's a reminder to give feedback to people. 1745 01:14:02,060 --> 01:14:03,800 Because if there's no feedback, you 1746 01:14:03,800 --> 01:14:07,760 don't know what the individual is taking away from that. 1747 01:14:07,760 --> 01:14:10,250 In the absence of it, own insecurities 1748 01:14:10,250 --> 01:14:12,570 will often come into play. 1749 01:14:12,570 --> 01:14:13,190 All right. 1750 01:14:13,190 --> 01:14:18,213 So other things that one can do is change the narrative. 1751 01:14:18,213 --> 01:14:19,880 Because, remember, as I was just saying, 1752 01:14:19,880 --> 01:14:23,030 people have their own insecurities going. 1753 01:14:23,030 --> 01:14:25,460 And so, sometimes you want to let 1754 01:14:25,460 --> 01:14:29,690 them know that perhaps other people have had insecurities 1755 01:14:29,690 --> 01:14:35,280 as well, and that it's OK, and that failing sometimes 1756 01:14:35,280 --> 01:14:37,850 is not only OK, it's part of the process. 1757 01:14:37,850 --> 01:14:41,750 So for high achievers, it's important to reassure them 1758 01:14:41,750 --> 01:14:45,630 that everyone has had difficulty at some point in their career. 1759 01:14:45,630 --> 01:14:47,270 So this serves as a counter narrative, 1760 01:14:47,270 --> 01:14:50,150 this story they're telling themselves, that somehow you 1761 01:14:50,150 --> 01:14:52,310 have to be perfect all the time to really 1762 01:14:52,310 --> 01:14:53,940 be successful in science. 1763 01:14:53,940 --> 01:14:57,650 You don't have to be, but we need to share those stories. 1764 01:14:57,650 --> 01:15:00,860 The CV, it's a list of successes. 1765 01:15:00,860 --> 01:15:01,780 It's a list of papers. 1766 01:15:01,780 --> 01:15:03,170 It's a list of awards. 1767 01:15:03,170 --> 01:15:08,310 It doesn't say on there, this is what I've done with my career. 1768 01:15:08,310 --> 01:15:09,020 No. 1769 01:15:09,020 --> 01:15:10,820 What you've done with your career, 1770 01:15:10,820 --> 01:15:14,390 between every success are a ton of failures. 1771 01:15:14,390 --> 01:15:17,240 But we don't list the things that don't work, 1772 01:15:17,240 --> 01:15:19,118 we just list the things that do work. 1773 01:15:19,118 --> 01:15:21,410 Our publications are full of the experiments that work, 1774 01:15:21,410 --> 01:15:23,300 not the experiments that didn't work. 1775 01:15:23,300 --> 01:15:25,220 So it's really easy for people to look 1776 01:15:25,220 --> 01:15:26,930 at someone who's successful and think, 1777 01:15:26,930 --> 01:15:28,340 they've never had to struggle. 1778 01:15:28,340 --> 01:15:30,360 It's been super easy for them. 1779 01:15:30,360 --> 01:15:31,700 They've never failed. 1780 01:15:31,700 --> 01:15:33,510 And that is just not the case. 1781 01:15:33,510 --> 01:15:36,080 So if we share those stories with the students, 1782 01:15:36,080 --> 01:15:39,710 it can help them realize that maybe this bad grade isn't 1783 01:15:39,710 --> 01:15:40,950 the end of the world. 1784 01:15:40,950 --> 01:15:41,910 It's all OK. 1785 01:15:41,910 --> 01:15:44,920 It's part of the process. 1786 01:15:44,920 --> 01:15:45,420 All right. 1787 01:15:45,420 --> 01:15:50,860 So this is the last exercise. 1788 01:15:50,860 --> 01:15:55,330 So recall a moment where a mentor shared a story with you 1789 01:15:55,330 --> 01:15:58,690 about a time when he or she struggled with a concept 1790 01:15:58,690 --> 01:16:01,390 or felt inadequate, but then reached 1791 01:16:01,390 --> 01:16:03,520 a high level of achievement, or you 1792 01:16:03,520 --> 01:16:05,230 could recall your own story. 1793 01:16:05,230 --> 01:16:07,060 So something that someone shared with you 1794 01:16:07,060 --> 01:16:10,510 that made a difference, or perhaps something that you 1795 01:16:10,510 --> 01:16:13,180 could tell your students this semester if they're 1796 01:16:13,180 --> 01:16:15,910 all of a sudden going, how did I get into this place? 1797 01:16:15,910 --> 01:16:17,920 I'm not sure that I belong here. 1798 01:16:17,920 --> 01:16:20,080 What could you share with them that 1799 01:16:20,080 --> 01:16:23,480 would help them move forward? 1800 01:16:23,480 --> 01:16:23,980 All right. 1801 01:16:23,980 --> 01:16:25,510 Find your partners. 1802 01:16:25,510 --> 01:16:27,706 Think about a time. 1803 01:16:27,706 --> 01:16:31,490 AUDIENCE: I think for a time when-- 1804 01:16:31,490 --> 01:16:33,410 at least a story that was a counter-narrative 1805 01:16:33,410 --> 01:16:37,480 for me was thinking about my own struggles in my PhD 1806 01:16:37,480 --> 01:16:41,727 where there was a long string of failures 1807 01:16:41,727 --> 01:16:43,310 where it felt like nothing was working 1808 01:16:43,310 --> 01:16:45,480 and I wasn't really getting anywhere. 1809 01:16:45,480 --> 01:16:48,840 And it was really hard to see the finish line, 1810 01:16:48,840 --> 01:16:52,030 and it got me really down. 1811 01:16:52,030 --> 01:16:57,110 It felt very difficult. I wasn't as happy as I could have been. 1812 01:16:57,110 --> 01:16:59,630 And I think what really helped me break out 1813 01:16:59,630 --> 01:17:01,910 of that was hearing from my own advisor 1814 01:17:01,910 --> 01:17:06,230 that she had had a pretty long and arduous PhD as well. 1815 01:17:06,230 --> 01:17:09,120 There was a lot of struggle. 1816 01:17:09,120 --> 01:17:11,570 And that those failures came in the exact same way, where 1817 01:17:11,570 --> 01:17:15,870 they're stacking on top of each other. 1818 01:17:15,870 --> 01:17:20,120 And it just feels like there is no end really in sight, 1819 01:17:20,120 --> 01:17:22,580 and maybe that you're not even good enough for this. 1820 01:17:22,580 --> 01:17:27,980 But seeing how far she made it, and how much she succeeded 1821 01:17:27,980 --> 01:17:31,190 after that really, really reassured me, and makes me 1822 01:17:31,190 --> 01:17:34,730 feel better about the whole thing. 1823 01:17:34,730 --> 01:17:37,430 It makes you realize everything's going to be OK. 1824 01:17:37,430 --> 01:17:39,240 This is not atypical. 1825 01:17:39,240 --> 01:17:41,870 It doesn't say something about me, personally. 1826 01:17:46,430 --> 01:17:48,110 Really what's reassuring is that it 1827 01:17:48,110 --> 01:17:49,652 feels like everything is really going 1828 01:17:49,652 --> 01:17:51,720 to work out at the end of it. 1829 01:17:51,720 --> 01:17:55,700 And that changed my entire attitude really about my PhD. 1830 01:17:55,700 --> 01:17:59,410 I feel tremendously better about everything since then. 1831 01:17:59,410 --> 01:18:01,052 AUDIENCE: That's very inspiring. 1832 01:18:01,052 --> 01:18:01,760 AUDIENCE: Mm-hmm. 1833 01:18:01,760 --> 01:18:05,150 AUDIENCE: Yeah, for me, I felt it a lot during undergrad, 1834 01:18:05,150 --> 01:18:07,850 and I still feel it somewhat in grad school, 1835 01:18:07,850 --> 01:18:12,830 but one experience was even having an opportunity 1836 01:18:12,830 --> 01:18:15,710 to participate in a summer program at MIT. 1837 01:18:15,710 --> 01:18:18,530 So I went to a community college, 1838 01:18:18,530 --> 01:18:21,590 and my parents couldn't afford to send me 1839 01:18:21,590 --> 01:18:23,367 to a top-tier university. 1840 01:18:23,367 --> 01:18:25,450 We didn't have money saved up for me to go to one, 1841 01:18:25,450 --> 01:18:29,210 and so the advice was to go to community college 1842 01:18:29,210 --> 01:18:33,320 first, establish your GPA, get some scholarships, 1843 01:18:33,320 --> 01:18:35,480 and then transfer to a four-year institute. 1844 01:18:35,480 --> 01:18:37,640 And while I was at that community college, 1845 01:18:37,640 --> 01:18:41,840 I didn't feel like I would have any major opportunities to go 1846 01:18:41,840 --> 01:18:46,250 and do research, especially at an institute like MIT. 1847 01:18:46,250 --> 01:18:49,370 And I came across the application for the MIT summer 1848 01:18:49,370 --> 01:18:52,520 research program through another program 1849 01:18:52,520 --> 01:18:54,770 that I was involved in at my community college. 1850 01:18:54,770 --> 01:18:56,637 And I was like, OK, I'll just apply to it. 1851 01:18:56,637 --> 01:18:58,220 They'll probably not accept me or even 1852 01:18:58,220 --> 01:19:00,137 consider my application, but I'll still do it. 1853 01:19:00,137 --> 01:19:01,130 And I did it. 1854 01:19:01,130 --> 01:19:01,923 I applied. 1855 01:19:01,923 --> 01:19:03,590 And I ended up getting into the program. 1856 01:19:03,590 --> 01:19:07,310 And that one opportunity really opened a lot of doors for me, 1857 01:19:07,310 --> 01:19:09,320 and now I'm able to be a graduate student here. 1858 01:19:09,320 --> 01:19:13,430 So, yeah, that was a moment where I really felt insecure, 1859 01:19:13,430 --> 01:19:16,510 and the whole imposter syndrome really affected me. 1860 01:19:16,510 --> 01:19:18,655 But I'm happy that I took that opportunity. 1861 01:19:18,655 --> 01:19:19,350 Mm-hmm. 1862 01:19:19,350 --> 01:19:22,850 AUDIENCE: Yeah, that's a really awesome story. 1863 01:19:22,850 --> 01:19:25,150 AUDIENCE: Thanks. 1864 01:19:25,150 --> 01:19:28,380 AUDIENCE: Yeah, so when I started college, 1865 01:19:28,380 --> 01:19:32,000 I got a really, really bad grade on one of my tests. 1866 01:19:32,000 --> 01:19:34,408 I think it was actually my second semester of college. 1867 01:19:34,408 --> 01:19:34,950 AUDIENCE: OK. 1868 01:19:34,950 --> 01:19:35,610 AUDIENCE: I called my dad. 1869 01:19:35,610 --> 01:19:37,250 And it was the first bad test I've ever 1870 01:19:37,250 --> 01:19:38,220 had in my entire life. 1871 01:19:38,220 --> 01:19:39,120 I was so upset. 1872 01:19:39,120 --> 01:19:42,320 I called my dad and I said, dad, dad, I'm so sorry! 1873 01:19:42,320 --> 01:19:44,320 I'm failing! 1874 01:19:44,320 --> 01:19:45,920 And I had a full-ride scholarship, 1875 01:19:45,920 --> 01:19:48,645 so I thought I was going to lose my scholarship because of one 1876 01:19:48,645 --> 01:19:49,145 bad test. 1877 01:19:49,145 --> 01:19:49,980 AUDIENCE: Yeah, I understand that. 1878 01:19:49,980 --> 01:19:51,772 AUDIENCE: And my dad said, no, no, it's OK. 1879 01:19:51,772 --> 01:19:53,000 It's totally fine. 1880 01:19:53,000 --> 01:19:54,560 When I was in college-- 1881 01:19:54,560 --> 01:19:57,850 the same exact college-- 1882 01:19:57,850 --> 01:20:02,630 I failed miserably my first test in this math class. 1883 01:20:02,630 --> 01:20:05,240 And I just worked really hard and studied, 1884 01:20:05,240 --> 01:20:07,730 and tried to talk to people, and made 1885 01:20:07,730 --> 01:20:09,570 sure that I knew exactly what I was doing. 1886 01:20:09,570 --> 01:20:12,830 And by the end of the semester, my teacher was like, 1887 01:20:12,830 --> 01:20:14,870 don't even come in for the final because you're 1888 01:20:14,870 --> 01:20:15,980 going to break the curve. 1889 01:20:15,980 --> 01:20:17,690 So you can do it. 1890 01:20:17,690 --> 01:20:20,280 One bad test is not the end of you. 1891 01:20:20,280 --> 01:20:23,000 AUDIENCE: Yeah, that's cool. 1892 01:20:23,000 --> 01:20:23,997 I wish my-- 1893 01:20:23,997 --> 01:20:25,080 AUDIENCE: It really helps. 1894 01:20:25,080 --> 01:20:26,810 AUDIENCE: Yeah, I can imagine that would really help. 1895 01:20:26,810 --> 01:20:27,380 Yeah. 1896 01:20:27,380 --> 01:20:32,718 So I guess, growing up, I didn't have female scientists-- 1897 01:20:32,718 --> 01:20:33,260 I don't know. 1898 01:20:33,260 --> 01:20:35,773 The media doesn't really portray female scientists, slash, 1899 01:20:35,773 --> 01:20:38,190 I didn't know anyone that had a PhD or anything like that. 1900 01:20:38,190 --> 01:20:40,067 So when I got to college, I knew I was always 1901 01:20:40,067 --> 01:20:41,900 interested in science, but I didn't actually 1902 01:20:41,900 --> 01:20:42,840 know what that meant. 1903 01:20:42,840 --> 01:20:45,800 And so, I was really lucky enough 1904 01:20:45,800 --> 01:20:48,410 to have a wonderful mentor in undergrad. 1905 01:20:48,410 --> 01:20:52,490 And she, was the department head of chemistry, 1906 01:20:52,490 --> 01:20:53,700 so that was really awesome. 1907 01:20:53,700 --> 01:20:56,510 But we got really close, and she would tell me 1908 01:20:56,510 --> 01:20:58,760 about her journey as a scientist, 1909 01:20:58,760 --> 01:21:02,510 and how she started out becoming a lecturer, 1910 01:21:02,510 --> 01:21:06,440 and raising a family, and being a woman in science, 1911 01:21:06,440 --> 01:21:08,990 and what that meant for people's perception, 1912 01:21:08,990 --> 01:21:10,880 and how she felt like she never really got 1913 01:21:10,880 --> 01:21:12,890 the respect that she deserved. 1914 01:21:12,890 --> 01:21:15,140 And she would always make sure that we referred to her 1915 01:21:15,140 --> 01:21:18,770 as doctor, because it was the fact that she 1916 01:21:18,770 --> 01:21:22,430 had worked so hard for it, and had not been taken seriously. 1917 01:21:22,430 --> 01:21:25,070 So she shared a lot of like how she 1918 01:21:25,070 --> 01:21:26,630 didn't feel like she got the respect 1919 01:21:26,630 --> 01:21:30,110 or deserved to almost be in this position of power. 1920 01:21:30,110 --> 01:21:32,860 But she was an extremely successful scientist, 1921 01:21:32,860 --> 01:21:36,110 and did wonderful things, and was department head of our 1922 01:21:36,110 --> 01:21:36,910 at our school. 1923 01:21:36,910 --> 01:21:39,550 And just really showed me that even 1924 01:21:39,550 --> 01:21:42,220 if you don't feel like you can do it, 1925 01:21:42,220 --> 01:21:44,090 or you're not getting the respect, 1926 01:21:44,090 --> 01:21:47,090 you can still be really successful. 1927 01:21:47,090 --> 01:21:52,990 And having those conversations and just hearing her story, 1928 01:21:52,990 --> 01:21:55,480 made me feel like, OK, I can do this, 1929 01:21:55,480 --> 01:21:59,320 and allowed me to address like my insecurities. 1930 01:21:59,320 --> 01:22:03,760 And her support where she felt like I could go to grad school, 1931 01:22:03,760 --> 01:22:06,280 was a main reason why I felt like I could actually do it. 1932 01:22:06,280 --> 01:22:09,370 Because she talked about, it wasn't in her plans, 1933 01:22:09,370 --> 01:22:11,050 but she still like went through it. 1934 01:22:11,050 --> 01:22:13,790 And, like, look, I'm a Professor now, 1935 01:22:13,790 --> 01:22:17,020 even though I don't necessarily feel like I should have been, 1936 01:22:17,020 --> 01:22:18,820 or could have at times. 1937 01:22:18,820 --> 01:22:21,410 And so, that was really awesome to hear 1938 01:22:21,410 --> 01:22:24,340 and really made me feel like grad school was an option 1939 01:22:24,340 --> 01:22:27,460 before I even got here. 1940 01:22:27,460 --> 01:22:30,100 And before, I didn't even think about grad school. 1941 01:22:30,100 --> 01:22:31,100 AUDIENCE: That's great. 1942 01:22:31,100 --> 01:22:34,090 I'm so glad that she was able to bring you here. 1943 01:22:34,090 --> 01:22:35,320 AUDIENCE: She's wonderful. 1944 01:22:35,320 --> 01:22:39,450 AUDIENCE: I think a good example, in my case is my mom. 1945 01:22:39,450 --> 01:22:44,605 So obviously, her experience is 30 years ago and in Korea, 1946 01:22:44,605 --> 01:22:47,170 so it might be not that reflective of how things are 1947 01:22:47,170 --> 01:22:48,590 done in the States right now. 1948 01:22:48,590 --> 01:22:53,200 But basically, it was a lot harder for her, as a woman, 1949 01:22:53,200 --> 01:22:56,110 to get a job than her male counterparts, 1950 01:22:56,110 --> 01:22:59,050 because people were worried that she might quit 1951 01:22:59,050 --> 01:23:01,030 when she gets married, when she gets pregnant, 1952 01:23:01,030 --> 01:23:02,350 when she has a baby. 1953 01:23:02,350 --> 01:23:06,520 And even after she got a baby and she 1954 01:23:06,520 --> 01:23:08,710 has continued working in her workplace, 1955 01:23:08,710 --> 01:23:11,440 she was continuously judged by other people. 1956 01:23:11,440 --> 01:23:13,510 Because when she worked hard, people 1957 01:23:13,510 --> 01:23:17,590 were blaming her for not putting in that much of a dedication 1958 01:23:17,590 --> 01:23:18,940 to her home. 1959 01:23:18,940 --> 01:23:21,940 But if she was not working hard enough, 1960 01:23:21,940 --> 01:23:24,400 people were like, why are you like not working hard enough? 1961 01:23:24,400 --> 01:23:26,560 And you should not pay too much of attention 1962 01:23:26,560 --> 01:23:27,950 to your kids and your family. 1963 01:23:27,950 --> 01:23:28,950 AUDIENCE: You can't win. 1964 01:23:28,950 --> 01:23:29,825 AUDIENCE: Yeah, yeah. 1965 01:23:29,825 --> 01:23:34,840 So she had to put in a lot more time and effort 1966 01:23:34,840 --> 01:23:38,440 into both her household and in her workplace 1967 01:23:38,440 --> 01:23:41,110 to fight with the negative stereotype, 1968 01:23:41,110 --> 01:23:42,410 and not to get blame. 1969 01:23:42,410 --> 01:23:45,940 So yeah, her story really touched me, 1970 01:23:45,940 --> 01:23:48,910 and just keeping me aware that those kinds of stereotypes 1971 01:23:48,910 --> 01:23:50,440 are going out there, yeah. 1972 01:23:50,440 --> 01:23:54,780 AUDIENCE: Yeah, I think the one mentor that's really stuck out 1973 01:23:54,780 --> 01:23:57,682 or has had a big impact on me, especially in grad school, 1974 01:23:57,682 --> 01:23:58,515 has been my advisor. 1975 01:24:01,480 --> 01:24:06,160 Especially when I came to MIT, I definitely 1976 01:24:06,160 --> 01:24:09,180 had a stereotype of what an MIT grad student should be. 1977 01:24:09,180 --> 01:24:10,870 And it was like, they should only 1978 01:24:10,870 --> 01:24:12,255 be focused on their academics. 1979 01:24:12,255 --> 01:24:12,880 AUDIENCE: Yeah. 1980 01:24:12,880 --> 01:24:15,670 AUDIENCE: And I had a lot of personal things 1981 01:24:15,670 --> 01:24:17,710 I was struggling with that I thought 1982 01:24:17,710 --> 01:24:21,550 I had to leave behind me when I would come into the workplace. 1983 01:24:21,550 --> 01:24:25,030 And it almost became this really overbearing burden 1984 01:24:25,030 --> 01:24:28,690 of, I had to keep this personal side of me a secret 1985 01:24:28,690 --> 01:24:32,260 and I could only have this academic side of me in public. 1986 01:24:32,260 --> 01:24:36,910 So having a mentor who is very open about her own struggles, 1987 01:24:36,910 --> 01:24:40,300 and who's willing to share more about her personal life, 1988 01:24:40,300 --> 01:24:43,210 made me feel more comfortable bringing my full self 1989 01:24:43,210 --> 01:24:45,370 to the table when I am in lab. 1990 01:24:45,370 --> 01:24:47,920 So that's definitely had a big impact on me. 1991 01:24:47,920 --> 01:24:48,760 AUDIENCE: Yeah. 1992 01:24:48,760 --> 01:24:51,790 I feel that the media is gradually 1993 01:24:51,790 --> 01:24:54,580 changing from showing the typical nerdy scientist 1994 01:24:54,580 --> 01:24:56,357 into showing that, hey, scientists also 1995 01:24:56,357 --> 01:24:57,190 have personal lives. 1996 01:24:57,190 --> 01:24:57,620 AUDIENCE: Yeah. 1997 01:24:57,620 --> 01:24:58,630 That they are people. 1998 01:24:58,630 --> 01:24:58,830 AUDIENCE: Yeah. 1999 01:24:58,830 --> 01:24:59,090 AUDIENCE: Yeah. 2000 01:24:59,090 --> 01:24:59,350 AUDIENCE: Yeah, yeah. 2001 01:24:59,350 --> 01:25:00,840 AUDIENCE: Yeah, I really like that. 2002 01:25:00,840 --> 01:25:01,695 So yeah. 2003 01:25:01,695 --> 01:25:02,320 AUDIENCE: Cool. 2004 01:25:02,320 --> 01:25:03,440 AUDIENCE: Cool, cool. 2005 01:25:03,440 --> 01:25:05,023 CATHY L. DRENNAN: So I'll just briefly 2006 01:25:05,023 --> 01:25:06,910 tell you one story for me, which, 2007 01:25:06,910 --> 01:25:09,460 if you don't feel comfortable sharing with your students, 2008 01:25:09,460 --> 01:25:11,200 you can share my stories. 2009 01:25:11,200 --> 01:25:11,920 That's fine. 2010 01:25:11,920 --> 01:25:14,360 I have a long list. 2011 01:25:14,360 --> 01:25:17,740 So one thing that you can tell people which sometimes helps 2012 01:25:17,740 --> 01:25:20,740 is that professor Drennan is dyslexic. 2013 01:25:20,740 --> 01:25:24,520 So I was diagnosed in first grade. 2014 01:25:24,520 --> 01:25:28,050 I was a very smart and articulate kid, 2015 01:25:28,050 --> 01:25:30,820 and I liked talking to adults. 2016 01:25:30,820 --> 01:25:33,123 And so, they assumed I was sort of smart, 2017 01:25:33,123 --> 01:25:34,540 but then I couldn't learn to read. 2018 01:25:34,540 --> 01:25:38,200 So I was in the upper group, and then the somewhat lower group, 2019 01:25:38,200 --> 01:25:42,400 and lower group, and the lowest group, and the remedial group. 2020 01:25:42,400 --> 01:25:45,070 And it took me to sixth grade until I learned how to read-- 2021 01:25:45,070 --> 01:25:46,540 second time through sixth grade. 2022 01:25:46,540 --> 01:25:49,100 I had to do that twice. 2023 01:25:49,100 --> 01:25:52,570 And so, I was told I would never be able to graduate 2024 01:25:52,570 --> 01:25:53,492 from high school. 2025 01:25:53,492 --> 01:25:55,450 And so, when I graduated from high school, then 2026 01:25:55,450 --> 01:25:56,170 I went to college. 2027 01:25:56,170 --> 01:25:57,337 And then I was like, ha, ha! 2028 01:25:57,337 --> 01:25:57,940 Look at me! 2029 01:25:57,940 --> 01:25:59,770 And then I went to grad school and then I kept going. 2030 01:25:59,770 --> 01:26:00,353 It's like, oh! 2031 01:26:00,353 --> 01:26:01,570 Faculty position at MIT! 2032 01:26:01,570 --> 01:26:03,220 Let's go! 2033 01:26:03,220 --> 01:26:08,643 And I just needed to prove that I could do these things, 2034 01:26:08,643 --> 01:26:09,310 but it was hard. 2035 01:26:09,310 --> 01:26:16,390 And there are many really embarrassing dyslexic mistakes 2036 01:26:16,390 --> 01:26:18,460 that I have made over the years too. 2037 01:26:18,460 --> 01:26:19,960 And when I first started here, I was 2038 01:26:19,960 --> 01:26:23,140 very nervous about anybody knowing that I was dyslexic, 2039 01:26:23,140 --> 01:26:25,310 and I have gotten beyond that. 2040 01:26:25,310 --> 01:26:28,500 So I've now been videotaped by a whole bunch 2041 01:26:28,500 --> 01:26:30,750 of the dyslexic associations, and done 2042 01:26:30,750 --> 01:26:33,340 all sorts of different things where I share my stories. 2043 01:26:33,340 --> 01:26:36,660 So if you don't want to share a story about something from you, 2044 01:26:36,660 --> 01:26:39,360 you can share my stories, even if they're my students. 2045 01:26:39,360 --> 01:26:43,530 I am cool with everybody knowing that I am dyslexic. 2046 01:26:43,530 --> 01:26:48,210 And if you're my TA, and I write words that are not 2047 01:26:48,210 --> 01:26:50,340 words on the board, or switch numbers around, 2048 01:26:50,340 --> 01:26:52,530 just say, "Professor Drennan, other way," 2049 01:26:52,530 --> 01:26:53,560 or something like that. 2050 01:26:53,560 --> 01:26:54,460 That's cool. 2051 01:26:54,460 --> 01:26:56,500 So feel free to share these stories. 2052 01:26:56,500 --> 01:26:58,920 And I think it's so important that students know there's 2053 01:26:58,920 --> 01:27:00,750 more than one path to success. 2054 01:27:00,750 --> 01:27:02,320 And that not everyone is the same, 2055 01:27:02,320 --> 01:27:04,237 and people may struggle with different things. 2056 01:27:04,237 --> 01:27:07,290 They may have different insecurities, but it's all OK. 2057 01:27:07,290 --> 01:27:11,310 And those struggles often make us who we are, 2058 01:27:11,310 --> 01:27:14,100 and that science doesn't work perfectly the first time. 2059 01:27:14,100 --> 01:27:17,340 We have to get in there and just keep working at it. 2060 01:27:17,340 --> 01:27:19,230 And the struggles we face in our own life 2061 01:27:19,230 --> 01:27:22,290 is often just a really good motivating factor 2062 01:27:22,290 --> 01:27:25,080 for doing some really great research. 2063 01:27:25,080 --> 01:27:25,580 All right. 2064 01:27:25,580 --> 01:27:29,750 So again, you're very welcome to share my stories. 2065 01:27:29,750 --> 01:27:32,910 I have extra ones, if you want to hear times 2066 01:27:32,910 --> 01:27:35,420 that I felt insecure. 2067 01:27:35,420 --> 01:27:39,050 But I heard a lot of discussion, so maybe people 2068 01:27:39,050 --> 01:27:42,590 feel like they are willing to share a story with us right 2069 01:27:42,590 --> 01:27:44,485 now? 2070 01:27:44,485 --> 01:27:45,500 Yeah? 2071 01:27:45,500 --> 01:27:47,780 AUDIENCE: Going off what we were discussing about how 2072 01:27:47,780 --> 01:27:50,660 in science, so much of what we hear, or what's told to us, 2073 01:27:50,660 --> 01:27:53,390 is all successes. 2074 01:27:53,390 --> 01:27:57,080 And also knowing that a lot of those failures, at least for me 2075 01:27:57,080 --> 01:27:58,905 personally, they kind of come in waves. 2076 01:27:58,905 --> 01:28:00,530 There's not just one failure at a time. 2077 01:28:00,530 --> 01:28:01,070 It's a lot. 2078 01:28:01,070 --> 01:28:01,730 [SOFT LAUGHTER] 2079 01:28:01,730 --> 01:28:06,980 And it really wore on me at the time, but what I think really 2080 01:28:06,980 --> 01:28:11,150 helped me was my own mentor telling me that she struggled 2081 01:28:11,150 --> 01:28:12,050 through her PhD. 2082 01:28:12,050 --> 01:28:13,490 It took a long time. 2083 01:28:13,490 --> 01:28:15,200 It was a slog. 2084 01:28:15,200 --> 01:28:17,210 It just reminded me that it's not always 2085 01:28:17,210 --> 01:28:19,280 a straight line to get to the finish. 2086 01:28:19,280 --> 01:28:24,470 And more importantly, it's that things will get better. 2087 01:28:24,470 --> 01:28:25,760 It'll all work out. 2088 01:28:25,760 --> 01:28:27,030 Everything's going to be OK. 2089 01:28:27,030 --> 01:28:31,522 And I think that really helped me continuing on to my PhD. 2090 01:28:31,522 --> 01:28:32,480 CATHY L. DRENNAN: Yeah. 2091 01:28:32,480 --> 01:28:33,830 Yeah, that's wonderful. 2092 01:28:33,830 --> 01:28:34,370 Yeah. 2093 01:28:34,370 --> 01:28:37,380 And I often think of the waves. 2094 01:28:37,380 --> 01:28:40,800 And it's like things are going really, really well, 2095 01:28:40,800 --> 01:28:43,790 and then it slows down, and then things are not working. 2096 01:28:43,790 --> 01:28:47,030 And if the amplitude was up and down and this, it's like, 2097 01:28:47,030 --> 01:28:47,810 I'll be OK. 2098 01:28:47,810 --> 01:28:50,985 But often it's up and then down, and then down, and then down, 2099 01:28:50,985 --> 01:28:53,060 and then down, and then down. 2100 01:28:53,060 --> 01:28:55,275 And you're like, in other people their amplitudes are 2101 01:28:55,275 --> 01:28:56,150 different in the lab. 2102 01:28:56,150 --> 01:28:58,025 And it's like, that person has stuff working. 2103 01:28:58,025 --> 01:28:59,540 This person has stuff working. 2104 01:28:59,540 --> 01:29:01,700 What is wrong? 2105 01:29:01,700 --> 01:29:03,613 But the thing is that over time, it's 2106 01:29:03,613 --> 01:29:05,780 like when it's down like this, then when it goes up, 2107 01:29:05,780 --> 01:29:08,420 it stays up longer also. 2108 01:29:08,420 --> 01:29:10,790 So it's like getting you there again. 2109 01:29:10,790 --> 01:29:14,972 So yeah, that's why these stories can be pretty powerful. 2110 01:29:14,972 --> 01:29:16,680 Anybody else have one they want to share? 2111 01:29:16,680 --> 01:29:17,180 Yeah? 2112 01:29:17,180 --> 01:29:20,630 AUDIENCE: I think why it's been difficult for me 2113 01:29:20,630 --> 01:29:22,700 during grad school, especially in the beginning, 2114 01:29:22,700 --> 01:29:26,550 was that I was dealing with some personal struggles 2115 01:29:26,550 --> 01:29:28,700 from before grad school and during. 2116 01:29:28,700 --> 01:29:31,280 And I kind of felt like I had this burden 2117 01:29:31,280 --> 01:29:34,430 of keeping it a secret, because I didn't want to come off 2118 01:29:34,430 --> 01:29:35,930 as overly emotional. 2119 01:29:35,930 --> 01:29:39,320 Or that because I was dealing with these personal struggles, 2120 01:29:39,320 --> 01:29:42,350 that I wasn't taking my sciences seriously. 2121 01:29:42,350 --> 01:29:44,510 So having a mentor that opened up 2122 01:29:44,510 --> 01:29:47,150 about how she had also had personal struggles 2123 01:29:47,150 --> 01:29:49,880 and how she still was able to be successful, 2124 01:29:49,880 --> 01:29:53,330 was really meaningful to me, to feel 2125 01:29:53,330 --> 01:29:56,122 like I could be both a person and a scientist. 2126 01:29:56,122 --> 01:29:57,330 CATHY L. DRENNAN: Yeah, yeah. 2127 01:29:57,330 --> 01:29:58,580 Yeah, yeah. 2128 01:29:58,580 --> 01:30:00,330 It is one package, right? 2129 01:30:00,330 --> 01:30:01,680 Yeah. 2130 01:30:01,680 --> 01:30:03,210 We're all a package of these things. 2131 01:30:03,210 --> 01:30:04,590 So yeah, that's wonderful. 2132 01:30:04,590 --> 01:30:08,460 And I think that sometimes it just looks to people 2133 01:30:08,460 --> 01:30:10,830 as if everything is going so well, 2134 01:30:10,830 --> 01:30:13,170 and they just don't see the inside struggles. 2135 01:30:13,170 --> 01:30:14,880 And you don't really know, necessarily, 2136 01:30:14,880 --> 01:30:15,960 what else is going on. 2137 01:30:15,960 --> 01:30:19,140 And so, when you're comfortable sharing these things, 2138 01:30:19,140 --> 01:30:20,348 it can be so powerful. 2139 01:30:20,348 --> 01:30:22,890 And I feel like sometimes when people are getting through it, 2140 01:30:22,890 --> 01:30:25,590 it's like, now you have to be a professor because you've 2141 01:30:25,590 --> 01:30:28,330 got to share these stories with other people. 2142 01:30:28,330 --> 01:30:30,882 And that figuring out how to deal with the struggle, I mean, 2143 01:30:30,882 --> 01:30:31,840 that's what it's about. 2144 01:30:31,840 --> 01:30:34,470 It's not about doing everything perfectly the first time. 2145 01:30:34,470 --> 01:30:36,630 It's how to deal with failure, get yourself 2146 01:30:36,630 --> 01:30:38,735 up, get through those times. 2147 01:30:38,735 --> 01:30:40,860 And that's what gives you the strength to really go 2148 01:30:40,860 --> 01:30:42,820 after doing hard stuff. 2149 01:30:42,820 --> 01:30:43,320 All right. 2150 01:30:43,320 --> 01:30:47,990 Are there any other ones? 2151 01:30:47,990 --> 01:30:48,580 OK. 2152 01:30:48,580 --> 01:30:49,080 All right. 2153 01:30:49,080 --> 01:30:50,740 Let's move on. 2154 01:30:50,740 --> 01:30:52,450 The bad news. 2155 01:30:52,450 --> 01:30:53,473 We all have stereotypes. 2156 01:30:53,473 --> 01:30:55,390 I think all of you are sitting there thinking, 2157 01:30:55,390 --> 01:30:59,130 man, let me just count all the different stereotypes I have. 2158 01:30:59,130 --> 01:31:00,130 We all have stereotypes. 2159 01:31:00,130 --> 01:31:01,970 We all have unconscious bias. 2160 01:31:01,970 --> 01:31:05,020 Now, maybe some of you will be like, oh, wait a minute. 2161 01:31:05,020 --> 01:31:06,490 Why did I react that way? 2162 01:31:06,490 --> 01:31:07,390 Oh, man! 2163 01:31:07,390 --> 01:31:10,360 There's another stereotype I have. 2164 01:31:10,360 --> 01:31:14,410 We all can be victims of stereotype threats or imposter 2165 01:31:14,410 --> 01:31:15,400 syndrome. 2166 01:31:15,400 --> 01:31:17,650 Stereotype threat can be pretty harmful. 2167 01:31:17,650 --> 01:31:20,110 It can lead to underperformance and a feeling 2168 01:31:20,110 --> 01:31:21,820 of being judged unfairly. 2169 01:31:21,820 --> 01:31:23,410 But there's good news. 2170 01:31:23,410 --> 01:31:25,750 You create an environment of trust in your classroom 2171 01:31:25,750 --> 01:31:28,540 or in your lab, give wise criticism, 2172 01:31:28,540 --> 01:31:32,410 and that can really mediate the negative effects of stereotype. 2173 01:31:32,410 --> 01:31:34,690 If you're part of a team doing something, 2174 01:31:34,690 --> 01:31:37,720 if someone believes that someone else believes in them-- they're 2175 01:31:37,720 --> 01:31:40,452 not sure they can do it, but my team believes in me, 2176 01:31:40,452 --> 01:31:42,160 therefore, I can't do it, because my team 2177 01:31:42,160 --> 01:31:44,080 is pretty smart. 2178 01:31:44,080 --> 01:31:45,520 These are all ways-- 2179 01:31:45,520 --> 01:31:49,220 and we have to help everyone reach their full potential. 2180 01:31:49,220 --> 01:31:51,490 The students you'll be working with this semester 2181 01:31:51,490 --> 01:31:54,160 are crazy, smart, and cool people. 2182 01:31:54,160 --> 01:31:56,410 And some of them may be insecure, 2183 01:31:56,410 --> 01:31:59,450 and you can really help them get to their full potential. 2184 01:31:59,450 --> 01:32:03,410 And it's an amazing opportunity to teach smart people. 2185 01:32:03,410 --> 01:32:05,260 So I hope you have a wonderful time, 2186 01:32:05,260 --> 01:32:08,770 and I hope that those horses that are dressed like zebras 2187 01:32:08,770 --> 01:32:10,960 in your classroom are feeling very zebra-ish 2188 01:32:10,960 --> 01:32:13,210 by the end of the semester. 2189 01:32:13,210 --> 01:32:14,960 So thank you very much for your attention. 2190 01:32:14,960 --> 01:32:17,240 I loved the stories you're shared today, 2191 01:32:17,240 --> 01:32:20,080 and have a wonderful semester. 2192 01:32:20,080 --> 01:32:22,830 [APPLAUSE]