1 00:00:05,280 --> 00:00:07,840 Speaker 1: Hey, this is Annie and Samantha, and welcome to stuff. 2 00:00:07,840 --> 00:00:20,880 Speaker 1: I never told your production of iHeart Radio. The day 3 00:00:21,200 --> 00:00:24,960 Speaker 1: this comes out is a leap day. Lappy leap Day, 4 00:00:25,160 --> 00:00:29,560 Speaker 1: Happy leap day. Yes, um. And since you could be 5 00:00:29,600 --> 00:00:32,559 Speaker 1: listening to this at any time, it is February twenty nine, 6 00:00:34,640 --> 00:00:38,720 Speaker 1: just to put a button on that. Yes, So I 7 00:00:38,800 --> 00:00:40,960 Speaker 1: keep I'm writing all kinds of wrong. Not only am 8 00:00:40,960 --> 00:00:44,000 Speaker 1: I writing twenty nineteen sixteen, and I'm going all over 9 00:00:44,040 --> 00:00:45,879 Speaker 1: the place. I'll try to put nineteen nineteen at one 10 00:00:45,920 --> 00:00:49,680 Speaker 1: point because I was doing twenty and I was like, 11 00:00:49,720 --> 00:00:53,080 Speaker 1: this is so wrong. I'm seeing right. Okay. That makes 12 00:00:53,080 --> 00:00:59,040 Speaker 1: me feel better, as does this this classic episode, because 13 00:00:59,280 --> 00:01:02,800 Speaker 1: it's one of my I I love math and science 14 00:01:02,800 --> 00:01:05,840 Speaker 1: and this is a story that I found really inspiring. 15 00:01:05,920 --> 00:01:09,199 Speaker 1: So for this classic episode, we're talking about NASA's Hidden 16 00:01:09,480 --> 00:01:13,560 Speaker 1: Computer Women are by we we mean past host Christian Caroline. 17 00:01:13,640 --> 00:01:17,080 Speaker 1: We are introducing it right. So obviously you guys know 18 00:01:17,200 --> 00:01:20,240 Speaker 1: Katherine Johnson passed away and she died at the age 19 00:01:20,240 --> 00:01:24,039 Speaker 1: of one oh one, which is incredible. Um. And she 20 00:01:24,400 --> 00:01:27,520 Speaker 1: was the mathematician that was depicted in Hidden Figures. So 21 00:01:27,560 --> 00:01:30,640 Speaker 1: this just seemed appropriate, not only for black History Month, 22 00:01:31,080 --> 00:01:33,880 Speaker 1: but because to celebrate her life and the amazing things 23 00:01:33,880 --> 00:01:36,080 Speaker 1: that she has done in her life and just the 24 00:01:36,080 --> 00:01:41,880 Speaker 1: barriers she broken alone exactly. So, without further delay, we 25 00:01:41,959 --> 00:01:48,360 Speaker 1: hope that you enjoy this classic Welcome to Stuff Mom 26 00:01:48,440 --> 00:01:57,200 Speaker 1: Never told you from House top Works dot Com. Hello, 27 00:01:57,240 --> 00:01:59,920 Speaker 1: and welcome to the podcast. I'm Kristen and I'm Carol 28 00:02:00,840 --> 00:02:05,000 Speaker 1: and listeners. This episode has it all. It's got space, 29 00:02:05,520 --> 00:02:08,880 Speaker 1: it's got science, it has amazing women you've never heard of, 30 00:02:08,960 --> 00:02:10,839 Speaker 1: and it also has a rage factor of the fact 31 00:02:10,880 --> 00:02:13,320 Speaker 1: that you've never heard of them and the double rage 32 00:02:13,360 --> 00:02:18,480 Speaker 1: factor of some racist working conditions. There are literal highs 33 00:02:18,520 --> 00:02:23,360 Speaker 1: I'm talking like into space in the space eyes and 34 00:02:23,560 --> 00:02:30,160 Speaker 1: lows i e. Racism and segregation. Yes, yeah, um uh. 35 00:02:30,320 --> 00:02:34,120 Speaker 1: This episode is I hope as fascinating to listen to 36 00:02:34,200 --> 00:02:37,160 Speaker 1: as it was for us to research. It really continues 37 00:02:37,360 --> 00:02:40,560 Speaker 1: the theme that we started in our episode on Polly 38 00:02:40,639 --> 00:02:47,160 Speaker 1: Murray last time, of women who have sort of been 39 00:02:47,200 --> 00:02:51,280 Speaker 1: buried by history, women who played a huge part in 40 00:02:51,480 --> 00:02:54,840 Speaker 1: our country's history. In this case, science history with Polly 41 00:02:54,919 --> 00:02:58,600 Speaker 1: Murray was legal history and racism in segregation. But the 42 00:02:58,639 --> 00:03:02,240 Speaker 1: thing is in all of these amazing women's stories for 43 00:03:02,280 --> 00:03:05,160 Speaker 1: so many of them, there's still so little known. We 44 00:03:05,280 --> 00:03:10,000 Speaker 1: have names. But it's taking a woman like Margot Lee Shutterley, 45 00:03:10,480 --> 00:03:14,320 Speaker 1: who is publishing this year the book Hidden Figures, the 46 00:03:14,400 --> 00:03:17,640 Speaker 1: untold story of the African American women who helped the 47 00:03:17,760 --> 00:03:21,560 Speaker 1: United States when the Space Race. It's taking Shutterley publishing 48 00:03:21,560 --> 00:03:24,720 Speaker 1: a book and then attracting movie attention because it will 49 00:03:24,800 --> 00:03:28,040 Speaker 1: be made into a movie next year to get these 50 00:03:28,080 --> 00:03:33,640 Speaker 1: women some more attention. Yeah, and we've talked before on 51 00:03:33,720 --> 00:03:38,320 Speaker 1: stuff Mo'm never told you about the history of computing 52 00:03:38,320 --> 00:03:41,920 Speaker 1: and how women were the first quote unquote computers doing 53 00:03:41,960 --> 00:03:47,240 Speaker 1: the computer work, which was considered like low level clerical work. Um. 54 00:03:47,400 --> 00:03:52,280 Speaker 1: But even in that knowledge that's only just now becoming 55 00:03:52,320 --> 00:03:55,560 Speaker 1: more commonly known. At first, the image was just of 56 00:03:55,880 --> 00:03:59,200 Speaker 1: white women doing all of this computing. And now thanks 57 00:03:59,240 --> 00:04:03,840 Speaker 1: to Shutterly and um a couple of other researchers that 58 00:04:03,920 --> 00:04:07,720 Speaker 1: she's collaborated with, we are learning about these women of 59 00:04:07,800 --> 00:04:11,480 Speaker 1: color who were also doing computing work but might have 60 00:04:11,520 --> 00:04:16,400 Speaker 1: been segregated in separate working areas, and who were critical 61 00:04:16,920 --> 00:04:19,080 Speaker 1: in the case of what we're going to talk about today, 62 00:04:19,200 --> 00:04:22,839 Speaker 1: to the space race, to us even landing on the Moon. 63 00:04:23,240 --> 00:04:26,080 Speaker 1: Maybe Space Race should be the penny name of this episode, 64 00:04:26,600 --> 00:04:30,880 Speaker 1: Space Race Race Space. Um. But I mean it's not 65 00:04:30,920 --> 00:04:34,039 Speaker 1: like these women's stories were secret or unknown by any means. 66 00:04:34,080 --> 00:04:39,760 Speaker 1: I mean what's so fascinating and like mind boggling is 67 00:04:39,800 --> 00:04:43,440 Speaker 1: that you'll see obituaries of some of these women and 68 00:04:43,480 --> 00:04:46,440 Speaker 1: it's like, you know, so and so passed away and 69 00:04:46,560 --> 00:04:49,600 Speaker 1: she was a family woman and she like did some 70 00:04:49,640 --> 00:04:53,120 Speaker 1: stuff for NASA and then she died and you're like 71 00:04:53,360 --> 00:04:57,479 Speaker 1: what wait, And so I mean these women's lives and 72 00:04:57,520 --> 00:05:00,360 Speaker 1: stories were no secret. I mean, Katherine Johnson, one woman 73 00:05:00,400 --> 00:05:03,480 Speaker 1: who will talk about, was just awarded the Presidential Medal 74 00:05:03,480 --> 00:05:07,800 Speaker 1: of Freedom by President Obama. So their their work is 75 00:05:07,839 --> 00:05:10,520 Speaker 1: definitely being recognized. But we want to do our part 76 00:05:11,000 --> 00:05:14,680 Speaker 1: as Sminty, to dig into this history a little bit 77 00:05:14,720 --> 00:05:20,640 Speaker 1: more and enlighten you. Yeah, so before we get into NASA, 78 00:05:20,800 --> 00:05:25,680 Speaker 1: we've got to talk about NAKA because the National Advisory 79 00:05:25,720 --> 00:05:29,719 Speaker 1: Committee for Aeronautics was the predecessor to NASA, and it 80 00:05:29,760 --> 00:05:34,120 Speaker 1: was formed in nineteen fifteen, which, by the way, was 81 00:05:34,440 --> 00:05:37,480 Speaker 1: only a little more than a decade after the airplane 82 00:05:37,520 --> 00:05:41,560 Speaker 1: was invented. Yeah, they got right on that. They're like, 83 00:05:41,920 --> 00:05:44,200 Speaker 1: I feel like this is important, we should probably do 84 00:05:44,240 --> 00:05:46,840 Speaker 1: something with this. So they were formed with the goal 85 00:05:46,920 --> 00:05:49,920 Speaker 1: of furthering aeronautical research, and in nineteen fifty eight would 86 00:05:49,920 --> 00:05:54,040 Speaker 1: become NASA, but first, in nineteen twenty, NAKA sets up 87 00:05:54,120 --> 00:05:58,120 Speaker 1: the Langley Memorial Research Center in Hampton, Virginia to study 88 00:05:58,160 --> 00:06:04,560 Speaker 1: aircraft and eventually space flights. Now geographical note that is 89 00:06:04,600 --> 00:06:10,400 Speaker 1: important to well. Note Hampton, Virginia is also home to 90 00:06:10,880 --> 00:06:15,760 Speaker 1: what was then called Hampton Institute, which is a historically 91 00:06:15,839 --> 00:06:20,440 Speaker 1: black college that had started around about the Civil War 92 00:06:20,880 --> 00:06:27,200 Speaker 1: to educate former slaves, and it grew into a university 93 00:06:27,279 --> 00:06:32,479 Speaker 1: which is still a historically black institution, and many people 94 00:06:32,839 --> 00:06:37,240 Speaker 1: in that college. In this case, lady people funneled right 95 00:06:37,279 --> 00:06:40,960 Speaker 1: from Hampton to NAKA and Langley. Yeah, I mean the 96 00:06:41,240 --> 00:06:45,919 Speaker 1: geographical location almost feels like destiny. And this was something 97 00:06:46,200 --> 00:06:50,960 Speaker 1: that Margot Lee Shutterley, who author while is writing right 98 00:06:50,960 --> 00:06:55,000 Speaker 1: now Hidden Figures Um notes because she grew up in Hampton, 99 00:06:55,520 --> 00:06:59,159 Speaker 1: I believe, and talks about how there were a lot 100 00:06:59,200 --> 00:07:03,159 Speaker 1: of African America families who were in STEM careers all 101 00:07:03,200 --> 00:07:06,640 Speaker 1: around her. It was very normalized. Yeah. Well, her dad 102 00:07:06,680 --> 00:07:11,120 Speaker 1: was also in science and she she talks about how, yes, 103 00:07:11,200 --> 00:07:14,560 Speaker 1: I knew black people who had any job, anything from 104 00:07:14,600 --> 00:07:17,240 Speaker 1: like a shoeshiner to a teacher, to a lawyer to 105 00:07:17,320 --> 00:07:20,680 Speaker 1: a doctor. But so many of the African Americans in 106 00:07:20,680 --> 00:07:24,560 Speaker 1: our community around us were in science and technology. And 107 00:07:24,600 --> 00:07:26,480 Speaker 1: she said, I just I thought it was what black 108 00:07:26,480 --> 00:07:30,160 Speaker 1: people did. I thought you just went into science. And 109 00:07:30,160 --> 00:07:32,440 Speaker 1: and I love that tidbit because I feel like Kristen, 110 00:07:32,480 --> 00:07:35,960 Speaker 1: anytime you and I talk about stem topics, we always 111 00:07:36,000 --> 00:07:40,480 Speaker 1: are very careful to point out how women entered the 112 00:07:40,520 --> 00:07:44,600 Speaker 1: field in the first place, because things like mentorship, people 113 00:07:44,640 --> 00:07:47,640 Speaker 1: who inspire you as a young person to get involved 114 00:07:47,640 --> 00:07:50,920 Speaker 1: in science, they're they're critical. That's a critical role to 115 00:07:51,040 --> 00:07:56,600 Speaker 1: inspire young people and to normalize a life spent pursuing science. 116 00:07:56,640 --> 00:08:01,240 Speaker 1: And so I love that Shutterley has this normalization of 117 00:08:01,240 --> 00:08:03,640 Speaker 1: science around her, as do some of the women will 118 00:08:03,640 --> 00:08:06,120 Speaker 1: talk about. Yeah, and it traces all the way back 119 00:08:06,280 --> 00:08:10,000 Speaker 1: to the Hampton Institute, which by the way, was book 120 00:08:10,040 --> 00:08:15,960 Speaker 1: Or T Washington's alma mater, and Hampton Institute was preceded 121 00:08:16,000 --> 00:08:20,480 Speaker 1: by the efforts of an African American woman of means 122 00:08:20,920 --> 00:08:25,360 Speaker 1: to educate freed slaves. Because originally in Virginia, there was 123 00:08:25,600 --> 00:08:29,920 Speaker 1: a law passive believe pre Civil War um forbidding black 124 00:08:30,000 --> 00:08:34,520 Speaker 1: people from getting educated. Yeah, so at great risk, this woman, 125 00:08:34,559 --> 00:08:39,320 Speaker 1: I believe her name is Margaret Peak. Yeah, started educating people, 126 00:08:39,440 --> 00:08:43,880 Speaker 1: and the institution even educated a group of Native Americans 127 00:08:43,920 --> 00:08:47,080 Speaker 1: who had been prisoner elsewhere in the country and sent 128 00:08:47,600 --> 00:08:50,400 Speaker 1: to Hampton. They're like, well, we don't we don't know 129 00:08:50,480 --> 00:08:53,240 Speaker 1: what to do with these people, so please educate them. 130 00:08:53,280 --> 00:08:56,760 Speaker 1: So Hampton has an amazing and very rich history of 131 00:08:56,840 --> 00:08:58,960 Speaker 1: educating people in this country. So, in other words, it 132 00:08:59,000 --> 00:09:02,680 Speaker 1: was a perfect place for NAKA to set up shop. 133 00:09:02,840 --> 00:09:08,719 Speaker 1: With the Langley Memorial Research Center in Langley hires its 134 00:09:08,760 --> 00:09:12,320 Speaker 1: first woman, Pearl Young, who becomes an engineer and a 135 00:09:12,440 --> 00:09:18,000 Speaker 1: technical editor at NAKA. So she broke the gender bearer there. Pearl. Yeah, 136 00:09:18,040 --> 00:09:21,920 Speaker 1: Pearl was the first lady person. But as NAKA is 137 00:09:21,920 --> 00:09:24,960 Speaker 1: picking up steam, they need more people. What do we do, well, 138 00:09:25,000 --> 00:09:28,040 Speaker 1: I guess we get cheap women with their tiny little 139 00:09:28,120 --> 00:09:31,880 Speaker 1: hands who can use the slide rules. So they began 140 00:09:32,000 --> 00:09:37,560 Speaker 1: hiring women computers in the nineteen thirties in earnest. But 141 00:09:38,840 --> 00:09:41,440 Speaker 1: it's interesting to think about that they called it a 142 00:09:41,480 --> 00:09:44,400 Speaker 1: computer pool in the same way that at a big office, 143 00:09:44,440 --> 00:09:48,600 Speaker 1: for instance, you might have a stenography pool. These women 144 00:09:48,600 --> 00:09:50,560 Speaker 1: could provide a lot of labor, do a lot of 145 00:09:50,600 --> 00:09:56,000 Speaker 1: calculations for relatively cheap and their primary task was to 146 00:09:56,400 --> 00:10:00,560 Speaker 1: do all the calculating and computation that engineers had been 147 00:10:00,640 --> 00:10:05,440 Speaker 1: doing for aircraft and later space missions in order to 148 00:10:05,600 --> 00:10:09,120 Speaker 1: free them up for other aspects of research. And the 149 00:10:09,160 --> 00:10:15,600 Speaker 1: engineers at this time were exclusively men. So the calculating 150 00:10:15,679 --> 00:10:21,320 Speaker 1: and computation, including things such as reading film of monometer boards, 151 00:10:21,320 --> 00:10:24,040 Speaker 1: which that just makes me think of the muppets phenomena 152 00:10:25,000 --> 00:10:30,440 Speaker 1: do do do do do? So the phenomena boards measured 153 00:10:30,520 --> 00:10:34,800 Speaker 1: pressure changes during wind tunnel resistance tests and recorded that 154 00:10:34,880 --> 00:10:37,719 Speaker 1: data on spreadsheets, and honestly, this was the part of 155 00:10:37,720 --> 00:10:40,120 Speaker 1: the research that took me right back to my high 156 00:10:40,160 --> 00:10:44,680 Speaker 1: school physics class and thanks washed over me. Well. Um, 157 00:10:44,720 --> 00:10:48,200 Speaker 1: I watched an interview with Christine Darden, who is another 158 00:10:48,200 --> 00:10:50,920 Speaker 1: woman we will talk about later in the podcast, But 159 00:10:51,160 --> 00:10:56,640 Speaker 1: as she's talking about spreadsheets, she's literally using hand motions 160 00:10:57,080 --> 00:11:00,560 Speaker 1: of like indicating how big these spread sheets were, and 161 00:11:00,559 --> 00:11:03,080 Speaker 1: that women just for hours and hours, day in and 162 00:11:03,160 --> 00:11:05,760 Speaker 1: day out, twenty four hours round the clock, we're entering 163 00:11:05,880 --> 00:11:08,360 Speaker 1: data and spreadsheets, and that just made my mind melt 164 00:11:08,400 --> 00:11:11,199 Speaker 1: a little. Oh god, it's like Excel I r L 165 00:11:12,960 --> 00:11:19,040 Speaker 1: by hand. So in addition to reading the mhenomenal boards, 166 00:11:19,120 --> 00:11:23,280 Speaker 1: which it is enometer listeners, but I prefer momenomena, I 167 00:11:23,280 --> 00:11:27,240 Speaker 1: prefer mhenomena as well, they also calculated rocket trajectories and 168 00:11:27,360 --> 00:11:31,800 Speaker 1: safe reentry angles, and in analyzing the data, they would 169 00:11:31,800 --> 00:11:38,160 Speaker 1: plot the results on graph paper in those giant spreadsheet booklets. 170 00:11:38,640 --> 00:11:40,920 Speaker 1: And all of this was done by hand, not with 171 00:11:40,960 --> 00:11:45,000 Speaker 1: their T I A D three pluses I had with 172 00:11:45,120 --> 00:11:48,360 Speaker 1: Frogger on it a T E D five. Yeah, you know, 173 00:11:48,920 --> 00:11:52,360 Speaker 1: I'm ritzy. It's the one time in my life when 174 00:11:52,360 --> 00:11:54,800 Speaker 1: I was ritzy. Um. But yeah, they would they would 175 00:11:54,920 --> 00:11:58,640 Speaker 1: use tools like slide rules, magnifying glasses, and and really 176 00:11:58,679 --> 00:12:01,480 Speaker 1: basic calculating machine to get all of this done. But 177 00:12:02,280 --> 00:12:05,000 Speaker 1: you know, lest you think that all they were doing 178 00:12:05,040 --> 00:12:08,400 Speaker 1: is writing stuff on spreadsheets, I mean, these women had 179 00:12:08,440 --> 00:12:11,880 Speaker 1: to have a very specialized knowledge to do this. They 180 00:12:12,000 --> 00:12:14,880 Speaker 1: ended up a lot of these women ended up devising 181 00:12:15,320 --> 00:12:19,520 Speaker 1: aeronautics and aerospace specific computing methods, and many went on 182 00:12:19,600 --> 00:12:21,719 Speaker 1: to write papers and books of their own. So it's 183 00:12:21,760 --> 00:12:24,560 Speaker 1: not like they're just sitting there day in the day 184 00:12:24,600 --> 00:12:30,400 Speaker 1: out mindlessly calculating. These are brilliant people and they were effective. 185 00:12:30,880 --> 00:12:36,160 Speaker 1: They were collectively praised for calculating data more rapidly and accurately, 186 00:12:36,280 --> 00:12:38,920 Speaker 1: doing more in the morning than an engineer alone could 187 00:12:38,920 --> 00:12:44,800 Speaker 1: finish in a day. Yeah, that's right, Lady brain's powerful. Well, 188 00:12:45,080 --> 00:12:49,760 Speaker 1: when World War two hits, NACA needs more people to 189 00:12:49,880 --> 00:12:53,319 Speaker 1: work to do these calculations, but the men folk are leaving. 190 00:12:53,400 --> 00:12:57,760 Speaker 1: So what happens there's a computer boom. They begin actively 191 00:12:57,840 --> 00:13:02,480 Speaker 1: recruiting more women workers. They were advertising in trade journals 192 00:13:02,920 --> 00:13:05,280 Speaker 1: and in pamphlets they sent to colleges, and they sent 193 00:13:05,360 --> 00:13:09,160 Speaker 1: recruiters out to college campuses, and some of those early 194 00:13:09,280 --> 00:13:12,920 Speaker 1: women computers were even in the group who went to 195 00:13:13,000 --> 00:13:15,480 Speaker 1: college campuses to try to be like sea ladies, you 196 00:13:15,520 --> 00:13:19,079 Speaker 1: can do maths just like I can professionally. And they 197 00:13:19,120 --> 00:13:21,480 Speaker 1: had a hand during this time in a lot of 198 00:13:21,480 --> 00:13:25,559 Speaker 1: critical projects, ranging uh from everything from World War two 199 00:13:25,600 --> 00:13:30,920 Speaker 1: aircraft testing to transnic and supersonic flight research. And because 200 00:13:31,080 --> 00:13:35,000 Speaker 1: this was happening during World War two, NAKA was running 201 00:13:35,120 --> 00:13:40,440 Speaker 1: twenty four seven, meaning that the computers these women would 202 00:13:40,480 --> 00:13:45,000 Speaker 1: work in eight hour shifts around the clock. So this 203 00:13:45,120 --> 00:13:51,080 Speaker 1: is disrupting the typical domestic setting for women at the time. 204 00:13:52,040 --> 00:13:56,839 Speaker 1: But here's a thing. Not surprisingly, just because these computers 205 00:13:56,840 --> 00:14:02,160 Speaker 1: worked harder and faster and we're also desperately needed, they 206 00:14:02,160 --> 00:14:06,560 Speaker 1: weren't making equal wages as the fellas, but it was 207 00:14:06,600 --> 00:14:09,760 Speaker 1: still gainful employment for women at the time. And this 208 00:14:09,840 --> 00:14:13,520 Speaker 1: is echoing our podcast way back when now about secretaries, 209 00:14:13,520 --> 00:14:16,560 Speaker 1: where it was like female secretaries would get paid so 210 00:14:16,640 --> 00:14:20,000 Speaker 1: much less than male secretaries, but what the women made 211 00:14:20,080 --> 00:14:22,640 Speaker 1: was still so much more than they could have made elsewhere. 212 00:14:22,720 --> 00:14:28,239 Speaker 1: So similarly, in the nineteen forties, women computers were classified 213 00:14:28,280 --> 00:14:33,880 Speaker 1: as sub professionals and could earn between a whopping one thousand, 214 00:14:34,040 --> 00:14:39,800 Speaker 1: four d forty dollars and thirty two dollars per year. Meanwhile, 215 00:14:40,520 --> 00:14:45,160 Speaker 1: dudes were usually hired as junior engineers, classified as professionals 216 00:14:45,360 --> 00:14:49,400 Speaker 1: and started out with dollars. And I keep in mind, though, 217 00:14:49,760 --> 00:14:53,960 Speaker 1: that these quote unquote sub professional women still had to 218 00:14:54,000 --> 00:14:57,760 Speaker 1: have at least a bachelor's degree, and many of them 219 00:14:57,800 --> 00:15:00,600 Speaker 1: had already served as high school teachers for years and 220 00:15:00,640 --> 00:15:03,520 Speaker 1: years before they entered this work. Many of them too, 221 00:15:03,600 --> 00:15:07,479 Speaker 1: as we'll talk about, we're very unsatisfied with their teaching careers. 222 00:15:07,480 --> 00:15:11,440 Speaker 1: But basically, like teaching and nursing were the avenues that 223 00:15:11,480 --> 00:15:14,880 Speaker 1: were open, uh, particularly to women of color, as we'll 224 00:15:14,880 --> 00:15:19,000 Speaker 1: get into in a bit, But this science work offered 225 00:15:19,040 --> 00:15:23,240 Speaker 1: them an entirely new path and a path into aeronautics. Yeah, 226 00:15:23,360 --> 00:15:27,080 Speaker 1: and quickly about the money to a teacher would be 227 00:15:27,160 --> 00:15:32,119 Speaker 1: making around five fifty dollars a year, So in that context, 228 00:15:32,200 --> 00:15:34,800 Speaker 1: this is good money, even though it's not equitable money. 229 00:15:34,840 --> 00:15:38,600 Speaker 1: But that doesn't mean that there weren't progressive policies in 230 00:15:38,680 --> 00:15:42,160 Speaker 1: place at Naka. Yeah. So we've talked a lot in 231 00:15:42,240 --> 00:15:46,920 Speaker 1: previous episodes about uh, particularly teaching, but also other fields 232 00:15:46,920 --> 00:15:51,360 Speaker 1: where at the time UH, if you got married and 233 00:15:51,600 --> 00:15:55,360 Speaker 1: or started a family, you were expected to leave. There 234 00:15:55,360 --> 00:16:00,560 Speaker 1: were policies against hiring married women, especially pregnant women, because 235 00:16:00,600 --> 00:16:03,040 Speaker 1: it was expected like the home is where you're supposed 236 00:16:03,080 --> 00:16:06,040 Speaker 1: to be, you're a woman. But Naka was like we 237 00:16:06,120 --> 00:16:11,480 Speaker 1: need you, you're so smart, please stay. So very importantly, 238 00:16:11,720 --> 00:16:16,120 Speaker 1: marriage and family based discrimination was not a thing at Naka. 239 00:16:16,680 --> 00:16:18,920 Speaker 1: They didn't have any of the whole like get rid 240 00:16:18,920 --> 00:16:21,560 Speaker 1: of women when they get married. And in fact, after 241 00:16:21,600 --> 00:16:23,960 Speaker 1: World War Two, when so many women were forced out 242 00:16:23,960 --> 00:16:26,600 Speaker 1: of the workplace once the men came home, Naka was like, 243 00:16:26,760 --> 00:16:29,000 Speaker 1: no, no no, no, can you please stay? You're so smart? 244 00:16:29,080 --> 00:16:31,160 Speaker 1: Can you please keep doing the math? Please do the 245 00:16:31,200 --> 00:16:34,600 Speaker 1: math well. And several of those computers were also married 246 00:16:34,640 --> 00:16:39,600 Speaker 1: to NACA engineers. So this, like you said, provided women 247 00:16:39,800 --> 00:16:45,360 Speaker 1: entry into aeronautics and provided an alternative and an exciting 248 00:16:45,480 --> 00:16:49,440 Speaker 1: alternative if you were into science and math. And this 249 00:16:49,680 --> 00:16:54,520 Speaker 1: wasn't just a job for white ladies. African American women 250 00:16:54,560 --> 00:16:58,760 Speaker 1: computers were part of naka's hiring push. And we will 251 00:16:58,840 --> 00:17:01,440 Speaker 1: introduce you to these women. We come right back from 252 00:17:01,440 --> 00:17:15,720 Speaker 1: a quick break. Now. Unfortunately, it did take a presidential 253 00:17:15,800 --> 00:17:20,880 Speaker 1: executive order for African American women to officially be part 254 00:17:20,960 --> 00:17:27,760 Speaker 1: of naka's hiring push. Um because back in nineteen forty one, 255 00:17:28,400 --> 00:17:33,479 Speaker 1: a Philip Randolph, who was a tremendous labor and civil 256 00:17:33,560 --> 00:17:38,760 Speaker 1: rights leader, pressured FDR into issuing Executive Order eight O 257 00:17:38,960 --> 00:17:43,040 Speaker 1: two Yeah, which says there shall be no discrimination in 258 00:17:43,040 --> 00:17:46,720 Speaker 1: the employment of workers in defense industries or government because 259 00:17:46,720 --> 00:17:51,640 Speaker 1: of race, creed, color, or national origin. So that just 260 00:17:51,920 --> 00:17:55,600 Speaker 1: opens the hiring floodgates. And again NAK is placing ads 261 00:17:55,600 --> 00:17:58,399 Speaker 1: and papers, sending recruiters out, but this time they're putting 262 00:17:58,440 --> 00:18:02,200 Speaker 1: ads in black communities. News papers, and they were putting 263 00:18:02,240 --> 00:18:07,360 Speaker 1: out ads for everyone from janitors to laborers to women 264 00:18:07,520 --> 00:18:11,480 Speaker 1: with math and chemistry degrees. So this means that they're 265 00:18:11,560 --> 00:18:17,119 Speaker 1: hiring smart African American women. But this is also the 266 00:18:17,160 --> 00:18:23,399 Speaker 1: era of segregation. So black women at Langley worked eight 267 00:18:23,560 --> 00:18:26,840 Speaker 1: and even use the restroom in separate facilities in what 268 00:18:26,920 --> 00:18:29,399 Speaker 1: was called the West Area, and they were referred to 269 00:18:29,760 --> 00:18:35,440 Speaker 1: as the West Area Computers, and their facilities were so 270 00:18:35,560 --> 00:18:39,520 Speaker 1: far removed from where the white computers worked. A lot 271 00:18:39,520 --> 00:18:43,080 Speaker 1: of those women, the white women, didn't even know that 272 00:18:43,119 --> 00:18:47,720 Speaker 1: the West Area Computers existed. Yeah, and I mean, so well, 273 00:18:47,800 --> 00:18:50,960 Speaker 1: it's wonderful that all of these women of color were hired. 274 00:18:51,000 --> 00:18:54,160 Speaker 1: I mean, it wasn't without its problems. For instance, if 275 00:18:54,200 --> 00:18:56,520 Speaker 1: you were a single white women, you got to live 276 00:18:56,680 --> 00:18:59,480 Speaker 1: in really nice dorms at the facility, but if you 277 00:18:59,520 --> 00:19:02,760 Speaker 1: were an unmarried black woman, you had to have the 278 00:19:02,840 --> 00:19:05,280 Speaker 1: extra expense of finding a place to stay in town. 279 00:19:05,440 --> 00:19:08,879 Speaker 1: And also the lab was built on the side of 280 00:19:08,880 --> 00:19:13,680 Speaker 1: a plantation. Yeah, yeah, that's that's unfortunate. But I mean, 281 00:19:13,720 --> 00:19:15,040 Speaker 1: what else are you going to do with all that 282 00:19:15,200 --> 00:19:19,119 Speaker 1: land in Virginia, I guess, but build amazing science facilities 283 00:19:19,160 --> 00:19:22,959 Speaker 1: on it? But build rocket ships exactly, just blast right 284 00:19:23,000 --> 00:19:26,920 Speaker 1: on out of that past. But even within NACO once 285 00:19:26,920 --> 00:19:30,200 Speaker 1: they got in the door, obviously they were facing these uh, 286 00:19:30,440 --> 00:19:35,320 Speaker 1: the segregation, and they also faced barriers to advancement compared 287 00:19:35,359 --> 00:19:38,520 Speaker 1: to their white counterparts. Um for instance, one woman hired 288 00:19:38,520 --> 00:19:41,800 Speaker 1: to work in a chemistry division ended up being transferred 289 00:19:42,320 --> 00:19:46,560 Speaker 1: to the all black West Computer Division because black women 290 00:19:46,680 --> 00:19:51,040 Speaker 1: weren't hired for that original chemistry spots. And this is 291 00:19:51,080 --> 00:19:55,600 Speaker 1: ironic since African American computers had to take a chemistry 292 00:19:55,640 --> 00:20:00,119 Speaker 1: course at nearby Hampton Institute before starting their jobs, So 293 00:20:00,200 --> 00:20:02,879 Speaker 1: they even had a higher bar that they had to 294 00:20:02,920 --> 00:20:06,320 Speaker 1: cross to get in the door. Yeah, and so how 295 00:20:06,520 --> 00:20:08,800 Speaker 1: did some of them end up moving up the ranks 296 00:20:08,800 --> 00:20:12,560 Speaker 1: if they were segregated off an entirely separate facilities as well, 297 00:20:13,480 --> 00:20:17,560 Speaker 1: it was the black computers, those West Area computers who 298 00:20:17,640 --> 00:20:21,919 Speaker 1: would be regularly quote unquote loaned two different branches of 299 00:20:22,040 --> 00:20:26,720 Speaker 1: NAKA when help was needed doing calculations or data processing. 300 00:20:27,440 --> 00:20:31,919 Speaker 1: And eventually the computing divisions did become less segregated, and 301 00:20:31,920 --> 00:20:37,080 Speaker 1: when NACA became NASA, those segregated facilities were completely done 302 00:20:37,080 --> 00:20:41,200 Speaker 1: away with. And we've been talking about the West Area 303 00:20:41,200 --> 00:20:46,600 Speaker 1: computers as one kind of impersonal group. But now comes 304 00:20:46,640 --> 00:20:48,639 Speaker 1: the fun part of the podcast where we get to 305 00:20:48,720 --> 00:20:55,439 Speaker 1: highlight these individual women who were doing all sorts of 306 00:20:55,480 --> 00:20:58,800 Speaker 1: incredible trailblazing work. And one of the first we're going 307 00:20:58,840 --> 00:21:03,160 Speaker 1: to talk about Miriam Daniel Man And she was born 308 00:21:03,160 --> 00:21:06,280 Speaker 1: in nineteen o seven in Georgia. She was a valedictorian 309 00:21:06,359 --> 00:21:08,840 Speaker 1: of her prep school. And then she graduated from Talladega 310 00:21:08,920 --> 00:21:12,560 Speaker 1: College with a chemistry major and math minor. What up 311 00:21:12,600 --> 00:21:15,959 Speaker 1: stem And she worked as a teacher for a few years. 312 00:21:16,359 --> 00:21:20,040 Speaker 1: Then she and her husband moved to Virginia, where he 313 00:21:20,080 --> 00:21:25,760 Speaker 1: gets a job at Hampton Institute Destiny, uh in nineteen 314 00:21:25,920 --> 00:21:29,119 Speaker 1: forty three, the same year that NACA puts out the 315 00:21:29,160 --> 00:21:33,000 Speaker 1: call for workers of color. She hears about the job 316 00:21:33,040 --> 00:21:36,560 Speaker 1: opening and Land's job. Of course, she and the other 317 00:21:36,960 --> 00:21:39,600 Speaker 1: colored computers as they were called, did have to take 318 00:21:39,640 --> 00:21:43,159 Speaker 1: that chemistry class at Hampton, which is kind of a 319 00:21:43,280 --> 00:21:47,920 Speaker 1: joke considering how qualified these women already were, I mean, valedictorian, 320 00:21:48,560 --> 00:21:53,320 Speaker 1: chemistry major, math minor. This is no dummy, um. And 321 00:21:53,800 --> 00:21:57,880 Speaker 1: the women were coming in Tanaka with just as many 322 00:21:57,960 --> 00:22:00,920 Speaker 1: qualifications as the men and still being had lesson how 323 00:22:00,960 --> 00:22:05,920 Speaker 1: to take a course blah. Anyway, I love the hints 324 00:22:05,960 --> 00:22:09,000 Speaker 1: of man's humor that we get from an account that 325 00:22:09,040 --> 00:22:13,280 Speaker 1: her daughter wrote. Her daughter remembers Miriam bringing home the 326 00:22:13,320 --> 00:22:17,920 Speaker 1: cafeteria sign that said colored, which of course then was replaced. 327 00:22:18,119 --> 00:22:19,840 Speaker 1: But I love that she was like, nope, swiping this, 328 00:22:20,600 --> 00:22:24,159 Speaker 1: taking this away now. I I hate segregation. Um. And 329 00:22:24,280 --> 00:22:27,680 Speaker 1: once NAKA became NASA, she was one of the computers 330 00:22:27,680 --> 00:22:32,200 Speaker 1: who worked on that Mercury program making calculations for John 331 00:22:32,200 --> 00:22:36,040 Speaker 1: Glenn's and Alan Shepherd's flights. And next up, we've got 332 00:22:36,080 --> 00:22:40,800 Speaker 1: to talk about Dorothy Vaughan. Because Dorothy Vaughn was not 333 00:22:40,880 --> 00:22:46,119 Speaker 1: gonna let white women as managers stop her, so she 334 00:22:46,280 --> 00:22:49,080 Speaker 1: was a prodigy hands down. She was born in Kansas 335 00:22:49,119 --> 00:22:53,560 Speaker 1: City in nine. She graduated from Wilbur Forest University in 336 00:22:53,640 --> 00:22:57,359 Speaker 1: nine and after working as a math teacher. Yet again 337 00:22:57,440 --> 00:23:01,000 Speaker 1: we have the teaching coming. First, she tired by Langley 338 00:23:01,040 --> 00:23:06,679 Speaker 1: Ine on the West Computers team, and in nineteen forty 339 00:23:06,800 --> 00:23:10,879 Speaker 1: nine she became the first black manager of the West 340 00:23:10,960 --> 00:23:13,439 Speaker 1: Computers and she was called the head Computer, which is 341 00:23:13,800 --> 00:23:18,080 Speaker 1: the coolest job title computer. Um. She was the first 342 00:23:18,320 --> 00:23:22,080 Speaker 1: black manager though of the West Computers at that time 343 00:23:22,119 --> 00:23:27,560 Speaker 1: because before then even the West Computers managers had all 344 00:23:27,600 --> 00:23:31,719 Speaker 1: been white women according to policy, right exactly, And so 345 00:23:31,800 --> 00:23:34,680 Speaker 1: this makes her the first black manager at NACA period, 346 00:23:35,119 --> 00:23:38,080 Speaker 1: and this granted her a much greater visibility and a 347 00:23:38,080 --> 00:23:42,600 Speaker 1: lot more opportunities for collaboration. She worked with other computers 348 00:23:42,640 --> 00:23:46,760 Speaker 1: on a handbook for algebraic methods and calculating machines. And 349 00:23:47,359 --> 00:23:49,480 Speaker 1: I know that, you know, when you get to talking 350 00:23:49,520 --> 00:23:52,879 Speaker 1: about managers, sometimes they can be jerks, but she was 351 00:23:52,920 --> 00:23:56,600 Speaker 1: not one of them. This was a beloved both mathematician 352 00:23:56,760 --> 00:24:00,959 Speaker 1: and manager because she advocated for the West Computers, but 353 00:24:01,400 --> 00:24:04,600 Speaker 1: she also intervened on behalf of women workers in general 354 00:24:04,680 --> 00:24:07,880 Speaker 1: when they deserved promotions. Are raises white or black. If 355 00:24:07,920 --> 00:24:10,639 Speaker 1: you worked hard and deserved to raise, Vaughan was going 356 00:24:10,680 --> 00:24:13,720 Speaker 1: to go to the map for you. And the engineers 357 00:24:13,840 --> 00:24:19,600 Speaker 1: valued her too. They took her personnel recommendations seriously, but 358 00:24:19,920 --> 00:24:22,720 Speaker 1: they often wanted her to be the one to work 359 00:24:22,760 --> 00:24:25,960 Speaker 1: on the more challenging work and calculations when they had 360 00:24:26,400 --> 00:24:28,400 Speaker 1: stuff to get done. They were like, oh, we really 361 00:24:28,400 --> 00:24:30,560 Speaker 1: appreciate to be you're recommending, Like, we we trust you 362 00:24:30,640 --> 00:24:33,520 Speaker 1: and we'll hire that person for this division, but could 363 00:24:33,520 --> 00:24:39,840 Speaker 1: you do it? Thanks? Dorothy uh In. NAKA transitions to NASA, 364 00:24:39,920 --> 00:24:44,040 Speaker 1: as we've mentioned, and the segregated facilities are shuttered. And 365 00:24:44,160 --> 00:24:47,159 Speaker 1: this is when Vaughan and many of the former West 366 00:24:47,240 --> 00:24:51,919 Speaker 1: computers are moved to electronic computing in the new Analysis 367 00:24:52,000 --> 00:24:58,280 Speaker 1: and Computation Division. When NASA gets the machine computeurs and 368 00:24:58,520 --> 00:25:04,000 Speaker 1: Vaughan continues her education, she becomes an expert in for Tran, 369 00:25:04,240 --> 00:25:07,600 Speaker 1: which is a computer programming language still in use today 370 00:25:07,640 --> 00:25:11,439 Speaker 1: for scientific computing. Oh and meanwhile she's also raising a 371 00:25:11,520 --> 00:25:13,880 Speaker 1: family and one of her children grow up to work 372 00:25:13,880 --> 00:25:18,320 Speaker 1: for NASA. Talk about awesome mom role model. I know, 373 00:25:19,080 --> 00:25:21,800 Speaker 1: I love it. I love that there's like a NASA 374 00:25:21,880 --> 00:25:27,360 Speaker 1: legacy in a family. It's just the family business, you know, NASA. UM. 375 00:25:27,440 --> 00:25:29,080 Speaker 1: And then the next one we want to talk about 376 00:25:29,240 --> 00:25:33,240 Speaker 1: is the amazing Catherine Johnson. She's the physicist and mathematician 377 00:25:33,359 --> 00:25:35,639 Speaker 1: who worked with both Man and Vaughan and who was 378 00:25:35,680 --> 00:25:40,680 Speaker 1: recently awarded the Presidential Medal of Freedom UM. But she 379 00:25:40,680 --> 00:25:42,600 Speaker 1: she was drawn to math from an early age. She 380 00:25:42,640 --> 00:25:46,440 Speaker 1: would write later, math it's just there. You're either right 381 00:25:46,560 --> 00:25:48,560 Speaker 1: or you're wrong, and that's what I like about it. 382 00:25:48,760 --> 00:25:52,480 Speaker 1: And she also has told numerous interviewers that I counted everything, 383 00:25:52,880 --> 00:25:56,840 Speaker 1: the steps, the dishes, the stars in the sky, and 384 00:25:56,920 --> 00:26:00,840 Speaker 1: her family growing up was pretty poor. They were so 385 00:26:01,040 --> 00:26:06,480 Speaker 1: invested in Katherine and her siblings education and very encouraging 386 00:26:07,040 --> 00:26:11,560 Speaker 1: of them. Um. And in school, she was a math 387 00:26:11,640 --> 00:26:15,800 Speaker 1: prodigy and she was inspired by her geometry teacher, Miss Turner. 388 00:26:15,960 --> 00:26:20,520 Speaker 1: Shout out to teachers and again more mentorship. Um. And 389 00:26:20,600 --> 00:26:25,480 Speaker 1: she graduated from high school at fourteen. But listen, her 390 00:26:25,480 --> 00:26:30,600 Speaker 1: parents were so invested in Katherine and her schooling that 391 00:26:30,840 --> 00:26:33,359 Speaker 1: because there was no high school for black children in 392 00:26:33,400 --> 00:26:36,800 Speaker 1: her original hometown, her parents sent her in her siblings 393 00:26:37,000 --> 00:26:40,560 Speaker 1: to a lab school on the all black West Virginia 394 00:26:40,640 --> 00:26:44,880 Speaker 1: State Institute campus. Yeah. So I mean no small feet 395 00:26:44,920 --> 00:26:48,280 Speaker 1: that this woman graduated from high school at fourteen. Yeah. 396 00:26:48,280 --> 00:26:51,280 Speaker 1: And then she graduated from college with degrees in math 397 00:26:51,320 --> 00:26:55,359 Speaker 1: and French at eighteen. In French, the brain is an 398 00:26:55,359 --> 00:26:58,520 Speaker 1: amazing thing, especially when it belongs to Katherine Johnson. Well, 399 00:26:58,560 --> 00:27:01,879 Speaker 1: and she even had more men tourship. In college. She 400 00:27:01,960 --> 00:27:07,200 Speaker 1: was taught by Dr William W. Chefflin Claytor, who spotted 401 00:27:07,200 --> 00:27:10,919 Speaker 1: her potential as a research mathematician, and he would go 402 00:27:10,960 --> 00:27:13,439 Speaker 1: on to to achieve a degree of fame for his 403 00:27:13,520 --> 00:27:16,359 Speaker 1: own research, And so I mean that reflects so amazingly 404 00:27:16,400 --> 00:27:19,080 Speaker 1: on Catherine that she has this person helping guide her 405 00:27:19,720 --> 00:27:23,280 Speaker 1: through her research. But in nineteen thirty nine, she was 406 00:27:23,320 --> 00:27:27,000 Speaker 1: actually one of three black students selected to integrate West 407 00:27:27,080 --> 00:27:31,560 Speaker 1: Virginia University's grad program. And she stayed just a year 408 00:27:31,680 --> 00:27:35,280 Speaker 1: before getting married and starting a family. And there's a 409 00:27:35,280 --> 00:27:40,879 Speaker 1: while here where she is raising her kids, she's teaching. 410 00:27:41,160 --> 00:27:43,040 Speaker 1: But then in nineteen fifty three, she ends up taking 411 00:27:43,040 --> 00:27:46,680 Speaker 1: a computer job at Langley because the year before her 412 00:27:46,720 --> 00:27:49,560 Speaker 1: husband had fallen ill and she needed to get back 413 00:27:49,600 --> 00:27:53,160 Speaker 1: to work. She had taken time off from teaching math, 414 00:27:53,240 --> 00:27:58,040 Speaker 1: French and music. So she gets in at Langley because 415 00:27:58,359 --> 00:28:04,480 Speaker 1: of naka's recruitment efforts for its Guidance and Navigation department. 416 00:28:04,720 --> 00:28:07,320 Speaker 1: And the first year though that she applied, they had 417 00:28:07,359 --> 00:28:10,960 Speaker 1: already filled their quota. So she reapplied the next year 418 00:28:11,000 --> 00:28:15,399 Speaker 1: and got in. So lesson learned if at first you 419 00:28:15,440 --> 00:28:17,800 Speaker 1: do not get the job, you can always reapply. Yeah, 420 00:28:17,880 --> 00:28:19,760 Speaker 1: especially if you get to work under Dorothy Vaughan like 421 00:28:19,800 --> 00:28:25,520 Speaker 1: she did. Yeah, And we're so lucky that Katherine Johnson reapplied, 422 00:28:25,600 --> 00:28:29,199 Speaker 1: because I mean, just weeks after she started in the 423 00:28:29,240 --> 00:28:34,480 Speaker 1: computer pool. She starts to just start asking questions about, Wait, 424 00:28:34,520 --> 00:28:37,560 Speaker 1: why are all of the people on the research team guys, Wait, 425 00:28:37,560 --> 00:28:41,240 Speaker 1: why aren't any of the women computers invited to these meetings? 426 00:28:41,520 --> 00:28:44,200 Speaker 1: Is there a policy or law against me not being 427 00:28:44,200 --> 00:28:46,840 Speaker 1: allowed to be here? No, okay, I'm just gonna be here. 428 00:28:46,840 --> 00:28:50,040 Speaker 1: I'm gonna ask a lot of questions. Yeah, it's amazing. 429 00:28:50,840 --> 00:28:55,840 Speaker 1: She She credits this habit of incessantly asking questions with 430 00:28:56,000 --> 00:28:59,320 Speaker 1: being asked to fill in on the all Dude flight 431 00:28:59,480 --> 00:29:03,680 Speaker 1: research team. She got to research airplane gust alleviation and 432 00:29:03,800 --> 00:29:08,040 Speaker 1: wake turbulence. And in nineteen fifty eight she moves from 433 00:29:08,080 --> 00:29:12,440 Speaker 1: the flight Mechanics branch to the spacecraft Controls branch, whose 434 00:29:12,520 --> 00:29:15,080 Speaker 1: job it was to get men into space like a SAP. 435 00:29:15,520 --> 00:29:19,320 Speaker 1: And it's in that space research division where she seriously 436 00:29:19,480 --> 00:29:22,400 Speaker 1: makes her mark and she doesn't waste any time. In 437 00:29:22,480 --> 00:29:27,000 Speaker 1: nineteen sixty she becomes the Flight Research Division's first credited 438 00:29:27,040 --> 00:29:30,840 Speaker 1: female author when she co authors a report with equations 439 00:29:30,840 --> 00:29:34,440 Speaker 1: that describe the trajectories for placing the manned mercury capsule 440 00:29:34,560 --> 00:29:38,479 Speaker 1: into low Earth orbit and bringing it back safely. And 441 00:29:38,520 --> 00:29:42,120 Speaker 1: then in nineteen sixty one she calculated the trajectory that 442 00:29:42,160 --> 00:29:46,880 Speaker 1: put the first American Alan shepherd into space. How am 443 00:29:47,360 --> 00:29:51,760 Speaker 1: I thirty one and just learning this incredible women's history fact. 444 00:29:51,800 --> 00:29:55,160 Speaker 1: Caroline Well, and I love that she has no lack 445 00:29:55,160 --> 00:29:58,520 Speaker 1: of confidence When she's looking back on this in two 446 00:29:58,600 --> 00:30:01,360 Speaker 1: thousand eight, she tells an inner you are the early 447 00:30:01,400 --> 00:30:03,880 Speaker 1: trajectory was a parabola, and it was easy to predict 448 00:30:03,880 --> 00:30:06,680 Speaker 1: where it would be at any point. Early on, when 449 00:30:06,720 --> 00:30:08,680 Speaker 1: they said they wanted the capsule to come down at 450 00:30:08,680 --> 00:30:10,480 Speaker 1: a certain place, they were trying to compute when it 451 00:30:10,480 --> 00:30:13,120 Speaker 1: should start, I said, let me do it. I just 452 00:30:13,160 --> 00:30:14,760 Speaker 1: like to imagine that that was her being like, oh, 453 00:30:14,800 --> 00:30:18,160 Speaker 1: for God's sake, she says, you tell me when you 454 00:30:18,200 --> 00:30:20,600 Speaker 1: want it and where you want it to land, and 455 00:30:20,640 --> 00:30:23,160 Speaker 1: I'll do it backwards and I'll tell you when to 456 00:30:23,240 --> 00:30:27,240 Speaker 1: take off. That was my forte. What a smooth operator. 457 00:30:27,520 --> 00:30:32,200 Speaker 1: Just be confident like Catherine so confident in nineteen sixty two, 458 00:30:32,800 --> 00:30:37,160 Speaker 1: despite the fact that actual computer machines by this point 459 00:30:37,160 --> 00:30:40,760 Speaker 1: we're being used to perform calculations. When it was John 460 00:30:40,800 --> 00:30:43,600 Speaker 1: Glenn's turn to go into space and orbit the Earth 461 00:30:43,680 --> 00:30:48,560 Speaker 1: in m A six, he specifically asked Johnson to run 462 00:30:48,600 --> 00:30:52,600 Speaker 1: the numbers against what the computer had come up with, 463 00:30:52,680 --> 00:30:56,360 Speaker 1: so in his pre flight checklist he had quote, have 464 00:30:56,600 --> 00:30:59,760 Speaker 1: the girl double check the numbers. Yeah, and she said 465 00:30:59,760 --> 00:31:01,920 Speaker 1: that it was very nervous, but I knew that my 466 00:31:02,000 --> 00:31:06,640 Speaker 1: calculations were correct, and they were so over an ounce 467 00:31:06,680 --> 00:31:11,480 Speaker 1: of Catherine's confidence. Uh. And in the mid sixties she 468 00:31:11,760 --> 00:31:15,080 Speaker 1: worked on trajectories for the Lunar Orbiter program, which mapped 469 00:31:15,080 --> 00:31:18,160 Speaker 1: the Moon's surface before the nineteen sixty nine moon landing, 470 00:31:18,200 --> 00:31:23,200 Speaker 1: speaking of which, in nineteen she calculated the trajectory for 471 00:31:23,240 --> 00:31:25,640 Speaker 1: the Apollo eleven flight to the Moon. And it doesn't 472 00:31:25,640 --> 00:31:32,040 Speaker 1: stop there. In seventy she collaborated on the backup calculation 473 00:31:32,200 --> 00:31:37,120 Speaker 1: that brought Apollo thirteen astronauts home safely. So it is 474 00:31:37,200 --> 00:31:41,200 Speaker 1: little wonder then that she was awarded the highest civilian 475 00:31:41,240 --> 00:31:45,760 Speaker 1: honor with the Presidential Medal of Freedom in November. And 476 00:31:45,760 --> 00:31:49,800 Speaker 1: there's so many other women at NAKA slash NASA slash 477 00:31:49,880 --> 00:31:54,440 Speaker 1: Langley to mention, but so little has been known about 478 00:31:54,480 --> 00:31:57,960 Speaker 1: them all these years. And that's actually something that hidden 479 00:31:58,000 --> 00:32:03,400 Speaker 1: Figures author Margot Lee shudderly is working to rectify. She 480 00:32:03,560 --> 00:32:08,600 Speaker 1: has teamed up with McAlister College researchers Duchess Harris, who 481 00:32:08,800 --> 00:32:12,920 Speaker 1: is the granddaughter of Miriam Daniel Man, one of the 482 00:32:12,960 --> 00:32:16,640 Speaker 1: first computers, and Lucy Short on what's called the Human 483 00:32:16,680 --> 00:32:21,560 Speaker 1: Computer Project, which is their digital effort to document the 484 00:32:21,640 --> 00:32:26,040 Speaker 1: lives and work of women who worked as computers at Langley. 485 00:32:27,320 --> 00:32:30,440 Speaker 1: And so there, I mean, there are names that we 486 00:32:30,600 --> 00:32:35,000 Speaker 1: know that I just wasn't able to find enough information about, 487 00:32:35,040 --> 00:32:38,680 Speaker 1: women like Sue Wilder, Eunice Smith, and the very prolific 488 00:32:38,720 --> 00:32:41,920 Speaker 1: writer and researcher Barbara Holly. All I know is her name, 489 00:32:42,000 --> 00:32:45,280 Speaker 1: and I know that she researched a lot of stuff 490 00:32:45,320 --> 00:32:49,000 Speaker 1: that my brain can barely comprehend um, But I just 491 00:32:49,080 --> 00:32:52,120 Speaker 1: don't have that much background. One of those women also 492 00:32:52,280 --> 00:32:56,360 Speaker 1: is Katherine Pedrew, who graduated from Storer College in ninety 493 00:32:56,440 --> 00:32:59,200 Speaker 1: three with a chemistry degree and went to work for 494 00:32:59,280 --> 00:33:02,840 Speaker 1: naka A imediately. She retired in nineteen six, and she 495 00:33:02,960 --> 00:33:07,000 Speaker 1: was involved in research on balance in the Instrument Research Division. 496 00:33:07,120 --> 00:33:10,600 Speaker 1: And that's about as much as I could find on Catherine. Well, 497 00:33:10,640 --> 00:33:14,320 Speaker 1: and we have more information on women who worked at 498 00:33:14,400 --> 00:33:19,760 Speaker 1: NASA in more recent years. Relatively, there's Melbo Roy Mountain, 499 00:33:19,920 --> 00:33:24,160 Speaker 1: who was Howard University alum. She earned a master's in math, 500 00:33:24,480 --> 00:33:28,800 Speaker 1: who became Assistant Chief of Research Programs at NASA's Trajectory 501 00:33:28,880 --> 00:33:33,280 Speaker 1: and Geodynamics Division in the nineteen sixties. And she I 502 00:33:33,320 --> 00:33:37,880 Speaker 1: mean at this point to consider how revolutionary it is 503 00:33:37,880 --> 00:33:39,920 Speaker 1: that she's the head of a team and not so 504 00:33:39,960 --> 00:33:43,960 Speaker 1: long ago you have Miriam Daniel Man taking down the 505 00:33:44,080 --> 00:33:50,000 Speaker 1: colored sign from the cafeteria spunk like. So, Mountain leads 506 00:33:50,000 --> 00:33:54,400 Speaker 1: a team of mathematicians who did the intense computational work 507 00:33:54,480 --> 00:33:59,240 Speaker 1: of launching and tracking echo satellites in Earth's orbit, and 508 00:33:59,320 --> 00:34:02,040 Speaker 1: Mountain help a lot of impressive jobs during her time 509 00:34:02,040 --> 00:34:06,640 Speaker 1: at NASA. In she was promoted to head programmer in 510 00:34:06,720 --> 00:34:11,160 Speaker 1: charge of the team that determined aircraft orbits in space. 511 00:34:11,719 --> 00:34:16,200 Speaker 1: She actually designed computer programs to predict trajectories and aircraft 512 00:34:16,239 --> 00:34:20,080 Speaker 1: locations before transitioning into the job that Kristen was talking about, 513 00:34:20,120 --> 00:34:23,840 Speaker 1: that assistant chief of Research Programs. And finally, we're going 514 00:34:23,920 --> 00:34:26,560 Speaker 1: to circle back to a name that you mentioned early 515 00:34:26,640 --> 00:34:30,719 Speaker 1: in the episode, Caroline Christine man Darden, who was the 516 00:34:30,800 --> 00:34:34,360 Speaker 1: first black woman at NASA Langley to be promoted into 517 00:34:34,480 --> 00:34:40,080 Speaker 1: senior executive service. And her bio is fantastic as well, 518 00:34:40,120 --> 00:34:44,360 Speaker 1: because going back to childhood, her interests in math was 519 00:34:44,400 --> 00:34:48,600 Speaker 1: sparked exploring how bikes and cars worked with her dad 520 00:34:48,840 --> 00:34:51,839 Speaker 1: rad dad alert. And here's another case of a kid 521 00:34:51,880 --> 00:34:55,440 Speaker 1: whose parents really valued her education. They ended up sending 522 00:34:55,480 --> 00:34:59,120 Speaker 1: her to a boarding school in Asheville, where a geometry 523 00:34:59,160 --> 00:35:02,799 Speaker 1: teacher he her fall in love with the subject, just 524 00:35:02,960 --> 00:35:08,640 Speaker 1: like Miss Turner. Katherine Johnson's geometry teacher. Yes, so important. 525 00:35:09,120 --> 00:35:12,080 Speaker 1: I like geometry too, and I hope they are geometry 526 00:35:12,080 --> 00:35:15,120 Speaker 1: teachers listening who feel very special right now. I also 527 00:35:15,440 --> 00:35:18,720 Speaker 1: loved geometry. That was my favorite math in high school. 528 00:35:18,960 --> 00:35:22,200 Speaker 1: I liked now. Granted, I literally was tutored in math 529 00:35:22,280 --> 00:35:24,520 Speaker 1: from the time I was in sixth grade to twelfth 530 00:35:24,520 --> 00:35:27,960 Speaker 1: grade because I am a word person. That's just how 531 00:35:28,000 --> 00:35:30,719 Speaker 1: it shook out. Um. But I really liked Algebran geometry, 532 00:35:30,719 --> 00:35:33,880 Speaker 1: Physics not so much, and chemistry I barely passed. So 533 00:35:34,080 --> 00:35:37,840 Speaker 1: I am impressed from all these women, to say the least. Um. 534 00:35:38,239 --> 00:35:43,520 Speaker 1: But in nineteen sixty two, Christine graduates from Hampton Institute 535 00:35:43,760 --> 00:35:47,560 Speaker 1: with a bachelor's in math education in a minor in 536 00:35:47,640 --> 00:35:50,120 Speaker 1: physics at just twenty, and she starts teaching. Because the 537 00:35:50,120 --> 00:35:53,360 Speaker 1: whole thing that she talks about in that interview, I 538 00:35:53,360 --> 00:35:56,960 Speaker 1: watched which I encourage you to google. Is that her 539 00:35:57,040 --> 00:36:01,320 Speaker 1: father was so concerned about his daughter not having a job, 540 00:36:01,960 --> 00:36:03,799 Speaker 1: and so he did what a lot of parents do, 541 00:36:03,920 --> 00:36:05,799 Speaker 1: and he's like, UM, I know, you have this one 542 00:36:05,840 --> 00:36:09,160 Speaker 1: interest which is cool, and that's math and that's awesome. However, 543 00:36:09,440 --> 00:36:11,480 Speaker 1: can you please major in something that will actually let 544 00:36:11,480 --> 00:36:16,720 Speaker 1: you get hired after college. So he strongly, strongly, strongly 545 00:36:17,040 --> 00:36:21,960 Speaker 1: encouraged his daughter to major in education because, as she 546 00:36:22,080 --> 00:36:24,960 Speaker 1: points out in the interview, like you know, they're just there. 547 00:36:25,080 --> 00:36:27,959 Speaker 1: Still weren't a ton of jobs open, like high paying 548 00:36:28,040 --> 00:36:31,839 Speaker 1: jobs and secure jobs open to women at the time. 549 00:36:31,960 --> 00:36:34,040 Speaker 1: So she's like, I, as much as I loved math, 550 00:36:34,400 --> 00:36:36,560 Speaker 1: I had to be prepared to do whatever I could 551 00:36:36,640 --> 00:36:38,560 Speaker 1: to make a living and helps for my family. And 552 00:36:38,680 --> 00:36:43,320 Speaker 1: so she does teach for a while. But in the meantime, 553 00:36:43,320 --> 00:36:48,239 Speaker 1: as she's teaching and still going to school, she's participating 554 00:36:48,280 --> 00:36:51,080 Speaker 1: in lunch counter sit in so she's participating in the 555 00:36:51,120 --> 00:36:53,880 Speaker 1: civil rights movement at the same time that she's you know, 556 00:36:54,080 --> 00:36:58,520 Speaker 1: pursuing math and eventually gonna shoot to NASA like a 557 00:36:58,640 --> 00:37:02,120 Speaker 1: rocket ship pocket ship up of awesome. So in nineteen 558 00:37:02,200 --> 00:37:06,200 Speaker 1: sixties seven, after studying physics and math at Virginia State College. 559 00:37:06,320 --> 00:37:09,480 Speaker 1: She earns her masters and math, and she's hired for 560 00:37:09,520 --> 00:37:13,200 Speaker 1: the Langley computer pool. But she is not going to 561 00:37:13,239 --> 00:37:17,560 Speaker 1: remain in that pool. She needs to cannonball into the 562 00:37:17,640 --> 00:37:23,080 Speaker 1: deep end. So in the seventies, as computers like machine 563 00:37:23,120 --> 00:37:25,720 Speaker 1: computers like we think of today, we're starting to become 564 00:37:25,719 --> 00:37:28,479 Speaker 1: a thing. Darten was one of the first to work 565 00:37:28,520 --> 00:37:33,759 Speaker 1: on developing computer programs because she'd studied programming in her masters, 566 00:37:33,800 --> 00:37:39,640 Speaker 1: so she began researching also Sonic Boom minimization, and after 567 00:37:39,760 --> 00:37:45,080 Speaker 1: five years in computing and programming support, she I mean 568 00:37:45,080 --> 00:37:47,799 Speaker 1: she just felt limited. She didn't feel like she had 569 00:37:47,920 --> 00:37:51,680 Speaker 1: enough knowledge of everything that she was working on, and 570 00:37:51,719 --> 00:37:54,799 Speaker 1: also she hadn't gotten promoted, so she was she was 571 00:37:54,840 --> 00:37:59,000 Speaker 1: getting a little anxious to do some new stuff. Yeah, 572 00:37:59,080 --> 00:38:02,279 Speaker 1: So in nineteen seven any two, she complains to the 573 00:38:02,320 --> 00:38:05,160 Speaker 1: section head about men who were coming in with similar 574 00:38:05,200 --> 00:38:09,200 Speaker 1: credentials but getting more opportunities for advancement. I mean, this 575 00:38:09,320 --> 00:38:11,319 Speaker 1: is the same kind of thing that we're hearing from 576 00:38:11,360 --> 00:38:13,839 Speaker 1: the very get go of women being hired first at 577 00:38:13,920 --> 00:38:17,840 Speaker 1: NAKA and then at NASA. So he transfers her to 578 00:38:18,040 --> 00:38:21,040 Speaker 1: Sonic Boom Research, like like you do well and I 579 00:38:21,080 --> 00:38:23,719 Speaker 1: think that she had even suggested to him because she 580 00:38:23,760 --> 00:38:27,080 Speaker 1: had started doing sonic boom research on her own, that 581 00:38:27,080 --> 00:38:30,959 Speaker 1: that's the direction that she should go in. And her 582 00:38:31,760 --> 00:38:35,200 Speaker 1: career path also echoes Katherine Johnson in the sense of 583 00:38:35,880 --> 00:38:39,000 Speaker 1: speaking up, calling attention to what you know and what 584 00:38:39,120 --> 00:38:42,960 Speaker 1: you would be good at, incessant questioning very important. Uh 585 00:38:43,000 --> 00:38:47,839 Speaker 1: In she earns a PhD in engineering and becomes one 586 00:38:47,880 --> 00:38:53,520 Speaker 1: of NASA's foremost experts on supersonic flight and sonic booms. 587 00:38:53,920 --> 00:38:57,799 Speaker 1: And one of the most endearing things about Johnson and 588 00:38:57,920 --> 00:39:01,440 Speaker 1: Darden's ten years at NASA is how they were not 589 00:39:01,520 --> 00:39:04,640 Speaker 1: only invested in their nine to five work, but also 590 00:39:04,760 --> 00:39:08,040 Speaker 1: in outreach and encouraging girls, in particular to get into 591 00:39:08,040 --> 00:39:11,120 Speaker 1: STEM like they did. Yeah, work that is so important, 592 00:39:11,160 --> 00:39:13,480 Speaker 1: and I mean, yes, the NASA work is important, but 593 00:39:13,520 --> 00:39:17,400 Speaker 1: that outreach work and providing visible role models to children 594 00:39:17,480 --> 00:39:20,920 Speaker 1: is so important, and that is something that NASA itself 595 00:39:20,960 --> 00:39:24,600 Speaker 1: has highlighted. They put out a report in um that 596 00:39:24,680 --> 00:39:27,480 Speaker 1: looked at data stretching from two thousand eight to twelve 597 00:39:27,960 --> 00:39:31,319 Speaker 1: that found that women of color make up just five 598 00:39:31,360 --> 00:39:36,080 Speaker 1: and a half percent of aerospace tech engineers, and they 599 00:39:36,120 --> 00:39:41,160 Speaker 1: linked this to quote inherent biases and stereotyping that exists 600 00:39:41,200 --> 00:39:44,000 Speaker 1: for women of color in the science and engineering workplace. 601 00:39:44,040 --> 00:39:46,880 Speaker 1: In addition, they write, there are challenges related to the 602 00:39:47,040 --> 00:39:52,560 Speaker 1: lack of mentorship and isolation, and so they strongly encourage 603 00:39:52,560 --> 00:39:56,239 Speaker 1: in this this report those same outreach efforts, but in 604 00:39:56,239 --> 00:39:59,040 Speaker 1: an official capacity. So, yes, it's so important to have 605 00:39:59,160 --> 00:40:02,120 Speaker 1: these visible figure is like Johnson and like Darden. But 606 00:40:02,239 --> 00:40:05,160 Speaker 1: now the this NASA report is saying, we have to 607 00:40:05,200 --> 00:40:09,120 Speaker 1: continue our efforts to create a pipeline. In the same 608 00:40:09,200 --> 00:40:12,960 Speaker 1: way that having Langley next door to Hampton created a 609 00:40:13,080 --> 00:40:17,919 Speaker 1: you know, a figurative pipeline of people geniuses to Langley. 610 00:40:18,719 --> 00:40:21,279 Speaker 1: NASA still needs to continue to do that kind of 611 00:40:21,320 --> 00:40:25,919 Speaker 1: work of encouraging boys and girls, but in this case 612 00:40:25,960 --> 00:40:28,800 Speaker 1: women of color to join their ranks and be among 613 00:40:28,840 --> 00:40:33,120 Speaker 1: their researchers. Yeah. I mean, one astounding statistic is that 614 00:40:33,160 --> 00:40:41,880 Speaker 1: there are around one hundred black female physicists in the US. Well, yeah, 615 00:40:42,000 --> 00:40:44,040 Speaker 1: so it's not just an issue at NASA. I mean 616 00:40:44,080 --> 00:40:48,239 Speaker 1: this is across the board um in the US and 617 00:40:48,719 --> 00:40:52,520 Speaker 1: likely abroad as well, although there are countries that are 618 00:40:52,840 --> 00:40:56,360 Speaker 1: far better in terms of gender equity in stem fields. 619 00:40:57,160 --> 00:41:01,560 Speaker 1: But now we're curious to know who your stem heroines are, 620 00:41:01,600 --> 00:41:05,359 Speaker 1: and especially the unsung ones, because as you can probably tell, 621 00:41:06,480 --> 00:41:09,800 Speaker 1: it really excites Caroline and me to learn about new 622 00:41:10,040 --> 00:41:13,239 Speaker 1: not new, but women we should have known about a 623 00:41:13,239 --> 00:41:16,400 Speaker 1: long time ago. We were doing all sorts of trailblazing 624 00:41:16,600 --> 00:41:22,200 Speaker 1: and brainpowering. Yeah, so I encourage all of you. If 625 00:41:22,239 --> 00:41:25,680 Speaker 1: you're at school or at work and you're starting to 626 00:41:25,760 --> 00:41:28,919 Speaker 1: doubt yourself, I want you to ask yourself, what would 627 00:41:28,960 --> 00:41:33,920 Speaker 1: Katherine Johnson do? She knew who she was, she knew 628 00:41:33,920 --> 00:41:35,759 Speaker 1: what she was good at, and she was not going 629 00:41:35,800 --> 00:41:42,040 Speaker 1: to stop asking questions or counting or counting, just counted everything. Well, Listeners, 630 00:41:42,080 --> 00:41:44,520 Speaker 1: you can email us mom Stuff at house stuff works 631 00:41:44,600 --> 00:41:47,320 Speaker 1: dot com, or you can also tweet us your thoughts 632 00:41:47,360 --> 00:41:50,759 Speaker 1: at mom Stuff Podcast, or send us a message on Facebook. 633 00:41:51,040 --> 00:41:53,000 Speaker 1: And we've got a couple of messages to share with 634 00:41:53,080 --> 00:42:03,840 Speaker 1: you right now. I have a letter here from Sarah 635 00:42:03,880 --> 00:42:07,600 Speaker 1: in response to our abortion episodes. She says, I love 636 00:42:07,680 --> 00:42:09,800 Speaker 1: the show and love that you ladies are so vocally 637 00:42:09,840 --> 00:42:12,000 Speaker 1: supportive of a woman's right to have an abortion if 638 00:42:12,000 --> 00:42:14,920 Speaker 1: she wishes. However, there seems to be this narrative of 639 00:42:14,960 --> 00:42:18,440 Speaker 1: the good abortion, and more specifically, the hard decision abortion 640 00:42:18,480 --> 00:42:21,440 Speaker 1: that I find frustrating. Yes, there are many women for 641 00:42:21,440 --> 00:42:24,239 Speaker 1: whom choosing to have an abortion is stressful, emotional, and 642 00:42:24,320 --> 00:42:26,880 Speaker 1: heart wrenching, but there are just as many women for 643 00:42:26,960 --> 00:42:30,080 Speaker 1: whom it is a no brainer decision. When I found 644 00:42:30,080 --> 00:42:32,160 Speaker 1: out I was pregnant at twenty one, I didn't even 645 00:42:32,160 --> 00:42:34,440 Speaker 1: have to think twice before calling an abortion clinic and 646 00:42:34,480 --> 00:42:37,719 Speaker 1: making an appointment. I have been using contraception because I 647 00:42:37,760 --> 00:42:39,600 Speaker 1: was not ready to have a child, and I had 648 00:42:39,640 --> 00:42:41,440 Speaker 1: always known that I would abort in the case of 649 00:42:41,440 --> 00:42:44,440 Speaker 1: an unplanned pregnancy. I feel like there is a cultural 650 00:42:44,480 --> 00:42:48,400 Speaker 1: perception of the good abortion, and it includes handwringing with 651 00:42:48,520 --> 00:42:51,040 Speaker 1: a long term boyfriend or a high stakes job or 652 00:42:51,040 --> 00:42:54,120 Speaker 1: a traumatic situation, and that simply isn't always the case. 653 00:42:54,719 --> 00:42:57,200 Speaker 1: Women should feel free and empowered to have an abortion 654 00:42:57,239 --> 00:42:59,520 Speaker 1: simply because they don't want to be pregnant, which I'm 655 00:42:59,520 --> 00:43:03,040 Speaker 1: sure you already know, so thank you, Sarah. Well. I've 656 00:43:03,080 --> 00:43:08,479 Speaker 1: got a letter here from Sierra about psychic women line 657 00:43:08,480 --> 00:43:11,160 Speaker 1: of employment far different than the NASA work we were 658 00:43:11,160 --> 00:43:15,439 Speaker 1: talking about in today's episode, but she writes about how 659 00:43:16,280 --> 00:43:19,680 Speaker 1: she briefly worked as a phone psychic when she was 660 00:43:19,760 --> 00:43:24,759 Speaker 1: having some trouble finding steadier employment, and she says you 661 00:43:24,760 --> 00:43:27,879 Speaker 1: would collect as much personal information from the collar as 662 00:43:27,920 --> 00:43:30,759 Speaker 1: you could before launching into the reading in order to 663 00:43:30,760 --> 00:43:35,200 Speaker 1: give your psychic forecast some degree of believable, very similitude, 664 00:43:35,480 --> 00:43:38,560 Speaker 1: and this may speak to why so many psychics are women. 665 00:43:39,080 --> 00:43:40,759 Speaker 1: The people who are in the company I worked for 666 00:43:40,840 --> 00:43:42,760 Speaker 1: were of the opinion that women were better at teasing 667 00:43:42,760 --> 00:43:45,840 Speaker 1: out the nuggets of personal intel that were so crucial 668 00:43:45,880 --> 00:43:50,040 Speaker 1: to creating a believable psychic experience. My collars were pretty 669 00:43:50,040 --> 00:43:53,240 Speaker 1: evenly split between men and women, but both groups seemed 670 00:43:53,280 --> 00:43:55,520 Speaker 1: more willing to open up to a female psychic and 671 00:43:55,560 --> 00:43:59,279 Speaker 1: divulge personal details without suspicion that I could then work 672 00:43:59,360 --> 00:44:01,960 Speaker 1: into my re eatings. I had a male roommate at 673 00:44:01,960 --> 00:44:03,600 Speaker 1: the time who worked at the same company, and he 674 00:44:03,640 --> 00:44:05,799 Speaker 1: had a harder time with callers getting aggressive when he 675 00:44:05,800 --> 00:44:08,960 Speaker 1: asked personal questions. I didn't work as a phone psychic 676 00:44:09,000 --> 00:44:11,800 Speaker 1: for long because it was a very sad job. I 677 00:44:11,880 --> 00:44:14,399 Speaker 1: knew I had no psychic abilities, and most people who 678 00:44:14,440 --> 00:44:17,600 Speaker 1: called were in desperate situations and just looking for someone 679 00:44:17,600 --> 00:44:20,400 Speaker 1: to tell them it would be okay. I felt terrible 680 00:44:20,400 --> 00:44:22,080 Speaker 1: about the fact that they were paying three dollars a 681 00:44:22,080 --> 00:44:24,200 Speaker 1: minute to get support during some of the darkest times 682 00:44:24,200 --> 00:44:27,000 Speaker 1: of their lives, and the company's only directive to me 683 00:44:27,120 --> 00:44:28,879 Speaker 1: was to keep callers on the line for as long 684 00:44:28,920 --> 00:44:32,960 Speaker 1: as possible. I also felt exploited because it's emotionally draining 685 00:44:33,000 --> 00:44:36,000 Speaker 1: to listen to the sad stories of strangers and concoct 686 00:44:36,080 --> 00:44:38,720 Speaker 1: b s designed only to fleece them out of money, 687 00:44:38,960 --> 00:44:41,240 Speaker 1: and I was only being paid twenty cents a minute. 688 00:44:41,800 --> 00:44:44,279 Speaker 1: The emotional labor the job demanded, combined with a lack 689 00:44:44,280 --> 00:44:48,160 Speaker 1: of financial security, was just too much and I quit 690 00:44:48,280 --> 00:44:52,080 Speaker 1: after a few months. I love the show well. Thanks 691 00:44:52,120 --> 00:44:55,840 Speaker 1: Sierra for sharing your experiences with us and now listeners, 692 00:44:55,880 --> 00:44:59,479 Speaker 1: we want to hear what's on your minds Mom. Stuff 693 00:44:59,520 --> 00:45:01,800 Speaker 1: at how so works dot com is our email address 694 00:45:02,200 --> 00:45:04,200 Speaker 1: and for links all of our social media as well 695 00:45:04,239 --> 00:45:07,279 Speaker 1: as all of our blogs, videos and podcasts with our 696 00:45:07,360 --> 00:45:11,520 Speaker 1: sources so you can learn more about the incredible women 697 00:45:11,560 --> 00:45:15,560 Speaker 1: of color who worked at NAKA and NASA. Head on 698 00:45:15,640 --> 00:45:23,759 Speaker 1: over to stuff Mom Never Told You dot com for 699 00:45:23,880 --> 00:45:26,200 Speaker 1: more on this and thousands of other topics. Is it 700 00:45:26,280 --> 00:45:35,200 Speaker 1: how staff works dot com