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