Elizabeth Spelke, in her debate with Steven Pinker, states: “...Steve argued that social forces are over-rated as causes of gender differences. Intrinsic differences in aptitude are a larger factor, and intrinsic differences in motives are the biggest factor of all. ... My own view is different. I think the big forces causing this gap are social factors. There are no differences in overall intrinsic aptitude for science and mathematics between women and men” (11). Based on the evidence provided by Spelke and Pinker and all the other authors in the reading set, do you agree with Pinker that “there is more than “a shred of evidence” for sex differences that are relevant to statistical gender disparities in elite hard science departments?” If yes, why? If not, why not?

Your essay must quote and/or paraphrase and work directly with material from all four essays in this reading set. In addition, define and employ key terms that seem to be central to the arguments of your sources and, therefore, to your argument as well. Primary among these key terms are “intrinsic differences” and “intrinsic aptitude.” Other key terms that might help you with your argument are: bias (subtle or overt); stereotype; socialization; empathizers vs. systematizers; mathematical reasoning; and spatial manipulation.

University of Massachusetts at Boston Colleges of Education and Human Development, Honors, Liberal Arts, Nursing and Health Sciences, Public and Community Service, and Science and Mathematics Writing Proficiency Evaluation (WPE): Timed-Essay Exam Reading Set C: Why Are There Still So Few Women in Science? Tuesday, January 2, 2015, at 10:00 AM Campus Center Ballroom (3rd Floor) Table of Contents 1. Pollack, Eileen. “Why Are There Still So Few Women in Science?” The New York Times Magazine, October 3, 2013, accessed April 7, 2015, http://www.nytimes.com/2013/10/06/magazine/whyare-there-still-so-few-women-in-science.html. 2. Pinker, Steven and Elizabeth Spelke. The Science of Gender and Science: A Debate. Mind/Brain/Behavior Initiative (MBB) at Harvard University, 22 April 2005. Web: 16 May 2005. < http://edge.org/> 3. Ceci, Stephen J., Donna K. Ginther, Shulamit Kahn, and Wendy M. Williams? “Women in Academic Science: A Changing Landscape.” Psychological Science in the Public Interest, 2014, Vol. 15(3) 75-141 4. Moss-Racusina, Corinne A., John F. Dovidio, Victoria L. Brescoll, Mark J. Grahama, and Jo Handelsman. “Science faculty’s subtle gender biases favor male students.” Proceedings of the National Academy of Sciences (PNAS)of the United States of America, October 9, 2012 vol. 109 no. 41 Articles reprinted with permission Notes: 1. Please check our web-site at http://www.umb.edu/wpe for the dates and times of the Writing Proficiency Workshops. The workshops will focus on strategies for developing a thesis, organizing an argument, and analyzing the reading sets. 2. Plagiarism in the timed essay, or in a portfolio, whether it is in the new essay or in one of the supporting essays, will be treated in the manner as outlined in the Code of Conduct. The consequences of violating these policies are serious and may include suspension or expulsion. 3. Please bring your student ID card with you to the exam; and, if you need or want a dictionary, it must be a hard copy. No electronic devices will be allowed in the exam; this includes cell-phones, blackberries, or electronic dictionaries. No bags, backpacks, or books of any kind will be allowed and must be checked before entering the testing room. 4. You must bring the reading set with you to the exam; you will not be provided a reading set if you do not bring your own. You may make brief notes on the printed side of the reading set, and on the “Notes” page provided in the set. You may not write on the back of the reading set or bring any other notes or books into the exam room. Why Are There Still So Few Women in Science By Eileen Pollack 1 Last summer, researchers at Yale published a study proving that physicists, chemists and biologists are likely to view a young male scientist more favorably than a woman with the same qualifications. Presented with identical summaries of the accomplishments of two imaginary applicants, professors at six major research institutions were significantly more willing to offer the man a job. If they did hire the woman, they set her salary, on average, nearly $4,000 lower than the man’s. Surprisingly, female scientists were as biased as their male counterparts. The new study goes a long way toward providing hard evidence of a continuing bias against women in the sciences. […] But what could still be keeping women out of the STEM fields (“STEM” being the current shorthand for “science, technology, engineering and mathematics”), which offer so much in the way of job prospects, prestige, intellectual stimulation and income? As one of the first two women to earn a bachelor of science degree in physics from Yale—I graduated in 1978—this question concerns me deeply. I attended a rural public school whose few accelerated courses in physics and calculus I wasn’t allowed to take because, as my principal put it, “girls never go on in science and math.” Angry and bored, I began reading about space and time and teaching myself calculus from a book. When I arrived at Yale, I was woefully unprepared. The boys in my introductory physics class, who had taken far more rigorous math and science classes in high school, yawned as our professor sped through the material, while I grew panicked at how little I understood. The only woman in the room, I debated whether to raise my hand and expose myself to ridicule, thereby losing track of the lecture and falling further behind. In the end, I graduated summa cum laude, Phi Beta Kappa, with honors in the major, having excelled in the department’s three-term sequence in quantum mechanics and a graduate course in gravitational physics, all while teaching myself to program Yale’s mainframe computer. But I didn’t go into physics as a career. At the end of four years, I was exhausted by all the lonely hours I spent catching up to my classmates, hiding my insecurities, struggling to do my problem sets while the boys worked in teams to finish theirs. […] Mostly, though, I didn’t go on in physics because not a single professor—not even the adviser who supervised my senior thesis—encouraged me to go to graduate school. Certain this meant I wasn’t talented enough to succeed in physics, I left the rough draft of my senior thesis outside my adviser’s door and slunk away in shame. Pained by the dream I had failed to achieve, I locked my textbooks, lab reports and problem sets in my father’s army footlocker and turned my back on physics and math forever. […] In many ways, of course, the climate has become more welcoming to young women who want to study science and math. Female students at the high school I attended in upstate New York no longer need to teach themselves calculus from a book, and the physics classes are taught by a charismatic young woman. When I first returned to Yale in the fall of 2010, everyone kept boasting that 30 to 40 percent of the undergraduates majoring in physics and physics-related fields were women. More remarkable, those young women studied in a department whose chairwoman was the formidable astrophysicist Meg Urry, who earned her Ph.D. from Johns Hopkins, completed a postdoctorate at M.I.T.’s center for space research and served on the faculty of the Hubble space telescope before Yale hired her as a full professor in 2001. (At the time, there wasn’t a single other female faculty member in the department.) In recent years, Urry has become devoted to using hard data and anecdotes from her own experience to alter her colleagues’ perceptions as to why there are so few women in the sciences. In response to the Summers controversy, she published an essay in The Washington Post describing her gradual 1 Eileen Pollack’s most recent novel is Breaking and Entering. She teaches creative writing at the University of Michigan. June 2015 WPE Reading Set C Page 1 of 23 realization that women were leaving the profession not because they weren’t gifted but because of the “slow drumbeat of being underappreciated, feeling uncomfortable and encountering roadblocks along the path to success.” […] Before we met, Urry predicted that the female students in her department would recognize the struggles she and I had faced but that their support system protected them from the same kind of selfdoubt. […] When I mentioned that a tea was being held that afternoon so I could interview female students interested in science and gender, Urry said she would try to attend. Judith Krauss, the professor who was hosting the tea (she is the former dean of nursing and now master of Silliman College, where I lived as an undergraduate), warned me that very few students would be interested enough to show up. When 80 young women (and three curious men) crowded into the room, Krauss and I were stunned. By the time Urry hurried in, she was lucky to find a seat. The students clamored to share their stories. One young woman had been disconcerted to find herself one of only three girls in her AP physics course in high school, and even more so when the other two dropped out. Another student was the only girl in her AP physics class from the start. Her classmates teased her mercilessly: “You’re a girl. Girls can’t do physics.” She expected the teacher to put an end to the teasing, but he didn’t. […] Although Americans take for granted that scientists are geeks, in other cultures a gift for math is often seen as demonstrating that a person is intuitive and creative. In 2008, the American Mathematical Society published data from a number of prestigious international competitions in an effort to track standout performers. The American competitors were almost always the children of immigrants, and very rarely female. For example, between 1959 and 2008, Bulgaria sent 21 girls to the International Mathematical Olympiad, while the U.S., from 1974, when it first entered the competition, to 2008, sent only 3; no woman even made the American team until 1998. According to the study’s authors, nativeborn American students of both sexes steer clear of math clubs and competitions because “only Asians and nerds” would voluntarily do math. “In other words, it is deemed uncool within the social context of U.S.A. middle and high schools to do mathematics for fun; doing so can lead to social ostracism. Consequently, gifted girls, even more so than boys, usually camouflage their mathematical talent to fit in well with their peers.” […] The most powerful determinant of whether a woman goes on in science might be whether anyone encourages her to go on. My freshman year at Yale, I earned a 32 on my first physics midterm. My parents urged me to switch majors. All they wanted was that I be able to earn a living until I married a man who could support me, and physics seemed unlikely to accomplish either goal. I trudged up Science Hill to ask my professor, Michael Zeller, to sign my withdrawal slip. I took the elevator to Professor Zeller’s floor, then navigated corridors lined with photos of the all-male faculty and notices for lectures whose titles struck me as incomprehensible. I knocked at my professor’s door and managed to stammer that I had gotten a 32 on the midterm and needed him to sign my drop slip. “Why?” he asked. He received D’s in two of his physics courses. Not on the midterms—in the courses. The story sounded like something a nice professor would invent to make his least talented student feel less dumb. In his case, the D’s clearly were aberrations. In my case, the 32 signified that I wasn’t any good at physics. “Just swim in your own lane,” he said. Seeing my confusion, he told me that he had been on the swimming team at Stanford. His stroke was as good as anyone’s. But he kept coming in second. “Zeller,” the coach said, “your problem is you keep looking around to see how the other guys are doing. Keep your eyes on your own lane, swim your fastest and you’ll win.” I gathered this meant he wouldn’t be signing my drop slip. “You can do it,” he said. “Stick it out.” I stayed in the course. Week after week, I struggled to do my problem sets, until they no longer seemed impenetrable. The deeper I now tunnel into my four-inch-thick freshman physics textbook, the more June 2015 WPE Reading Set C Page 2 of 23 equations I find festooned with comet-like exclamation points and theorems whose beauty I noted with exploding novas of hot-pink asterisks. The markings in the book return me to a time when, sitting in my cramped dorm room, I suddenly grasped some principle that governs the way objects interact, whether here on earth or light years distant, and I marveled that such vastness and complexity could be reducible to the equation I had highlighted in my book. Could anything have been more thrilling than comprehending an entirely new way of seeing, a reality more real than the real itself? I earned a B in the course; the next semester I got an A. By the start of my senior year, I was at the top of my class, with the most experience conducting research. But not a single professor asked me if I was going on to graduate school. When I mentioned shyly to Professor Zeller that my dream was to apply to Princeton and become a theoretician, he shook his head and said that if you went to Princeton, you had better put your ego in your back pocket, because those guys were so brilliant and competitive that you would get that ego crushed, which made me feel as if I weren’t brilliant or competitive enough to apply. Not even the math professor who supervised my senior thesis urged me to go on for a Ph.D. I had spent nine months missing parties, skipping dinners and losing sleep, trying to figure out why waves—of sound, of light, of anything—travel in a spherical shell, like the skin of a balloon, in any odddimensional space, but like a solid bowling ball in any space of even dimension. When at last I found the answer, I knocked triumphantly at my adviser’s door. Yet I don’t remember him praising me in any way. I was dying to ask if my ability to solve the problem meant that I was good enough to make it as a theoretical physicist. But I knew that if I needed to ask, I wasn’t. Years later, when I contacted that same professor, the mathematician Roger Howe, he responded enthusiastically to my request that we get together to discuss women in science and math. We met at his office, in a building that still has a large poster of famous mathematicians (all male) in the lobby, although someone has tacked a smaller poster of “famous women in math” on the top floor beside the women’s bathroom. Howe appeared remarkably youthful, even when you consider that when I studied with him, he was the youngest full professor at Yale. He suggested we grab a sandwich, and as we sat waiting for our panini, I told him that one reason I didn’t go to graduate school was that I compared myself with him and judged my talents wanting. After all, I’d had such a difficult time solving the problem he had challenged me to solve. He looked puzzled. “But you solved it.” “Yeah,” I said. “At the end I really understood what I was doing. But it took me such a long time.” “But that’s just how it is,” he said. “You don’t see it until you do, and then you wonder why you didn’t see it all along.” But I had needed to drop my class in real analysis. Howe shrugged. There are a lot of different math personalities. Different mathematicians are good at different fields. I asked if he had noticed any differences between the ways male and female students approach math problems, whether they have different “math personalities.” No, he said. Then again, he couldn’t get inside his students’ heads. He did have two female students go on in math, and both had done fairly well. I asked why even now there were no female professors on Yale’s math faculty. No tenured women, Howe corrected me. In 2010, the department voted to hire a woman for a tenure-track job. (That woman has yet to come up for tenure, but this year the faculty did hire a senior female professor.) Well, I said, that’s still not very many. He stared into the distance. “I guess I just haven’t seen that many women whose work I’m excited about.” I watched him mull over his answer, the way I used to watch him visualize n-dimensional toruses cradled in his hands. “Maybe women are victims of June 2015 WPE Reading Set C Page 3 of 23 misperception,” he said finally. Not long ago, one of his colleagues at another school admitted to him that back when all of them were starting out, there were two people in his field, a woman and a man, and this colleague assumed the man must be the better mathematician, but the woman has gone on to do better work. I finally came straight out and asked what he thought of my project. How did it compare with all the other undergraduate research projects he must have supervised? His eyebrows lifted, as if to express the mathematical symbol for puzzlement. Actually, he hadn’t supervised more than two or three undergraduates in his entire career. “It’s very unusual for any undergraduate to do an independent project in mathematics,” he said. “By that measure, I would have to say that what you did was exceptional.” “Exceptional?” I echoed. Then why had he never told me? The question took him aback. I asked if he ever specifically encouraged any undergraduates to go on for Ph.D.’s; after all, he was now the director of undergraduate studies. But he said he never encouraged anyone to go on in math. “It’s a very hard life,” he told me. “You need to enjoy it. There’s a lot of pressure being a mathematician. The life, the culture, it’s very hard.” When I told Meg Urry that Howe and several other of my professors said they don’t encourage anyone to go on in physics or math because it’s such a hard life, she blew raspberries. “Oh, come on,” she said. “They’re their own bosses. They’re well paid. They love what they do. Why not encourage other people to go on in what you love?” She gives many alumni talks, “and there’s always a woman who comes up to me and says the same thing you said, I wanted to become a physicist, but no one encouraged me. If even one person had said, ‘You can do this.’” She laughed. “Women need more positive reinforcement, and men need more negative reinforcement. Men wildly overestimate their learning abilities, their earning abilities. Women say, ‘Oh, I’m not good, I won’t earn much, whatever you want to give me is O.K.’ ” […] Not long ago, I met five young Yale alumnae at a Vietnamese restaurant in Cambridge. Three of the women were attending graduate school at Harvard—two in physics and one in astronomy—and two were studying oceanography at M.I.T. None expressed anxiety about surviving graduate school, but all five said they frequently worried about how they would teach and conduct research once they had children. […] What most young women don’t realize, Urry said, is that being an academic provides a female scientist with more flexibility than most other professions. She met her husband on her first day at the Goddard Space Flight Center. “And we have a completely equal relationship,” she told me. “When he looks after the kids, he doesn’t say he’s helping me.” No one is claiming that juggling a career in physics while raising children is easy. But having a family while establishing a career as a doctor or a lawyer isn’t exactly easy either, and that doesn’t prevent women from pursuing those callings. Urry suspects that raising a family is often the excuse women use when they leave science, when in fact they have been discouraged to the point of giving up. All Ph.D.’s face the long slog of competing for a junior position, writing grants and conducting enough research to earn tenure. Yet women running the tenure race must leap hurdles that are higher than those facing their male competitors, often without realizing any such disparity exists. In the mid-1990s, three senior female professors at M.I.T. came to suspect that their careers had been hampered by similar patterns of marginalization. They took the matter to the dean, who appointed a committee of six senior women and three senior men to investigate their concerns. After performing the investigation and studying the data, the committee concluded that the marginalization experienced by female scientists at M.I.T. “was often accompanied by differences in salary, space, awards, resources and response to outside offers between men and women faculty, with women receiving less June 2015 WPE Reading Set C Page 4 of 23 despite professional accomplishments equal to those of their colleagues.” The dean concurred with the committee’s findings. And yet, as was noted in the committee’s report, his fellow administrators “resisted the notion that there was any problem that arose from gender bias in the treatment of the women faculty. Some argued that it was the masculine culture of M.I.T. that was to blame, and little could be done to change that.” In other words, women didn’t become scientists because science—and scientists—were male. The committee’s most resonant finding was that the discrimination facing female scientists in the final quarter of the 20th century was qualitatively different from the more obvious forms of sexism addressed by civil rights laws and affirmative action, but no less real. [...B]roader studies show that the perception of discrimination is often accompanied by a very real difference in the allotment of resources. In February 2012, the American Institute of Physics published a survey of 15,000 male and female physicists across 130 countries. In almost all cultures, the female scientists received less financing, lab space, office support and grants for equipment and travel, even after the researchers controlled for differences other than sex. “In fact,” the researchers concluded, “women physicists could be the majority in some hypothetical future yet still find their careers experience problems that stem from often unconscious bias.” […] And yet the argument that women are underrepresented in the sciences because they know they will be happier in “people” fields strikes me as misdirected. The problem is that most girls—and boys—decide they don’t like math and science before those subjects reveal their true beauty, a condition worsened by the unimaginative ways in which science and math are taught. Last year, the President’s Council of Advisers on Science and Technology issued an urgent plea for substantial reform if we are to meet the demand for one million more STEM professionals than the United States is currently on track to produce in the next decade. But beyond strengthening our curriculum, we need to make sure that we stop losing girls at every step as they fall victim to their lack of self-esteem, their misperceptions as to who does or doesn’t go on in science and their inaccurate assessments of their talents. […] The key to reform is persuading educators, researchers and administrators that broadening the pool of female scientists and making the culture more livable for them doesn’t lower standards. If society needs a certain number of scientists, Urry said, and you can look for those scientists only among the males of the population, you are going to have to go much farther toward the bottom of the barrel than if you also can search among the females in the population, especially the females who are at the top of their barrel. […] As the Yale study laid bare—scientists of both sexes also need to realize that they can’t always see the way their bias affects their day-to-day lives. Abigail Stewart, director of the University of Michigan’s Advance program, which seeks to improve the lives of female and minority faculty members, told me in an e-mail that Handelsman’s study shakes the passionately held belief of most scientists that they are devoted to accurately identifying and nurturing merit in their students. “Evidence that we are not as likely to recognize and encourage talent (even modest talent, as in this study) shakes our confidence and (I hope) will make us more attentive to our limitations in recognizing talent where we don’t expect to find it.” Like Stewart, Urry thinks Handelsman’s study might catalyze the changes she has been agitating to achieve for years. “I’ve thought for a long time that understanding this implicit bias exists is critical. If you believe the playing field is equal, then any action you take is privileging women. But if you know that women are being undervalued, then you must do something, because otherwise you will be losing people who are qualified.” […] June 2015 WPE Reading Set C Page 5 of 23 As so many studies have demonstrated, success in math and the hard sciences, far from being a matter of gender, is almost entirely dependent on culture—a culture that teaches girls math isn’t cool and no one will date them if they excel in physics; a culture in which professors rarely encourage their female students to continue on for advanced degrees; a culture in which success in graduate school is a matter of isolation, competition and ridiculously long hours in the lab; a culture in which female scientists are hired less frequently than men, earn less money and are allotted fewer resources. And yet, as I listened to these four young women laugh at the stereotypes and fears that had discouraged so many others, I was heartened that even these few had made it this far, that theirs will be the faces the next generation grows up imagining when they think of a female scientist. The Science of Gender and Science: A Debate By Steven Pinker vs. Elizabeth Spelke 2 Steven Pinker For those of you who just arrived from Mars, there has been a certain amount of discussion here at Harvard on a particular datum, namely the under-representation of women among tenure-track faculty in elite universities in physical science, math, and engineering. […] As with many issues in psychology, there are three broad ways to explain this phenomenon. One can imagine an extreme “nature” position: that males but not females have the talents and temperaments necessary for science. […] The extreme nature position has no serious proponents. There is an extreme “nurture” position: that males and females are biologically indistinguishable, and all relevant sex differences are products of socialization and bias. Then there are various intermediate positions: that the difference is explainable by some combination of biological differences in average temperaments and talents interacting with socialization and bias. […] The nature and source of sex differences are of practical importance. Most of us agree that there are aspects of the world, including gender disparities, that we want to change. But if we want to change the world we must first understand it, and that includes understanding the sources of sex differences. […] Okay, so what are the similarities and differences between the sexes? There certainly are many similarities. Men and women show no differences in general intelligence […]. Also, when it comes to the basic categories of cognition—how we negotiate the world and live our lives; our concept of objects, of numbers, of people, of living things, and so on—there are no differences. Indeed, in cases where there are differences, there are as many instances in which women do slightly better than men as ones in which men do slightly better than women. For example, men are better at throwing, but women are more dexterous. Men are better at mentally rotating shapes; women are better at visual memory. Men are better at mathematical problem-solving; women are better at mathematical calculation. And so on. But there are at least six differences that are relevant to the datum we have been discussing. […] The first difference, long noted by economists studying employment practices, is that men and women differ in what they state are their priorities in life. To sum it up: men, on average, are more likely to chase status at the expense of their families; women give a more balanced weighting. Once again: Think statistics! The finding is not that women value family and don’t value status. It is not that men value status and don’t value family. Nor does the finding imply that every last woman has the 2 Steven Pinker is the Johnstone Family Professor in the Department of Psychology at Harvard University and Elizabeth S. Spelke is Berkman Professor of Psychology at Harvard University, where she is Co-Director of the Mind, Brain, and Behavior Initiative. June 2015 WPE Reading Set C Page 6 of 23 asymmetry that women show on average or that every last man has the asymmetry that men show on average. But in large data sets, on average, an asymmetry is what you find. […] Second, interest in people versus things and abstract rule systems. There is a staggering amount of data on this trait, because there is an entire field that studies people’s vocational interests. […] And this field has documented that there are consistent differences in the kinds of activities that appeal to men and women in their ideal jobs. I’ll just discuss one of them: the desire to work with people versus things. There is an enormous average difference between women and men in this dimension, about one standard deviation. And this difference in interests will tend to cause people to gravitate in slightly different directions in their choice of career. The occupation that fits best with the “people” end of the continuum is “director of a community services organization.” The occupations that fit best with the “things” end are physicist, chemist, mathematician, computer programmer, and biologist. We see this consequence not only in the choice of whether to go into science, but also in the choice which branch of science the two sexes tend to go into. Needless to say, from 1970 to 2002 there was a huge increase in the percentage of university degrees awarded to women. But the percentage still differs dramatically across fields. Among the Ph.Ds awarded in 2001, for example, in education 65% of the doctorates went to women; in the social sciences, 54%; in the life sciences, 47%; in the physical sciences, 26%; in engineering, 17%. This is completely predictable from the difference in interests between people and living things, on the one hand, and inanimate objects, on the other. And the pattern is pretty much the same in 1980 and 2001, despite the change in absolute numbers. Third, risk. Men are by far the more reckless sex. In a large meta-analysis involving 150 studies and 100,000 participants, in 14 out of 16 categories of risk-taking, men were over-represented. The two sexes were equally represented in the other two categories, one of which was smoking, for obvious reasons. And two of the largest sex differences were in “intellectual risk taking” and “participation in a risky experiment.” […] Fourth, three-dimensional mental transformations: the ability to determine whether the drawings in each of these pairs the same 3-dimensional shape. Again I’ll appeal to a meta-analysis, this one containing 286 data sets and 100,000 subjects. The authors conclude, “we have specified a number of tests that show highly significant sex differences that are stable across age, at least after puberty, and have not decreased in recent years.” Now, as I mentioned, for some kinds of spatial ability, the advantage goes to women, but in “mental rotation,” “spatial perception,” and “spatial visualization” the advantage goes to men. Now, does this have any relevance to scientific achievement? We don’t know for sure, but there’s some reason to think that it does. In psychometric studies, three-dimensional spatial visualization is correlated with mathematical problem-solving. And mental manipulation of objects in three dimensions figures prominently in the memoirs and introspections of most creative physicists and chemists, including Faraday, Maxwell, Tesla, Kéekulé, and Lawrence, all of whom claim to have hit upon their discoveries by dynamic visual imagery and only later set them down in equations. […] Fifth, mathematical reasoning. Girls and women get better school grades in mathematics and pretty much everything else these days. And women are better at mathematical calculation. But consistently, men score better on mathematical word problems and on tests of mathematical reasoning, at least statistically. Again, here is a meta analysis, with 254 data sets and 3 million subjects. It shows no significant difference in childhood; this is a difference that emerges around puberty, like many secondary sexual characteristics. But there are sizable differences in adolescence and adulthood, especially in high-end samples. […] Indeed, contrary to Liz [Elizabeth Spelke], and the popular opinion of many intellectuals, the tests are surprisingly good. There is an enormous amount of data on the predictive power of the SAT. For June 2015 WPE Reading Set C Page 7 of 23 example, people in science careers overwhelmingly scored in 90th percentile in the SAT or GRE math test. And the tests predict earnings, occupational choice, doctoral degrees, the prestige of one’s degree, the probability of having a tenure-track position, and the number of patents. Moreover this predictive power is the same for men and for women. As for why there is that underprediction of grades—a slight under-prediction, one-tenth of a standard deviation—the Educational Testing Service did a study on that phenomenon, and were able to explain the mystery by a combination of the choice of major, which differs between the sexes, and the greater conscientiousness of women. Finally there’s a sex difference in variability. It’s crucial here to look at the right samples. Estimates of variance depend highly on the tails of the distribution, which by definition contain smaller numbers of people. Since people at the tails of the distribution in many surveys are likely to be weeded out for various reasons, it’s important to have large representative samples from national populations. In this regard the gold standard is the Science paper by Novell and Hedges, which reported six large stratified probability samples. They found that in 35 out of 37 tests, including all of the tests in math, space, and science, the male variance was greater than the female variance. […] Now the fact that these six gender differences exist does not mean that they are innate. This of course is a much more difficult issue to resolve. A necessary preamble to this discussion is that nature and nurture are not alternatives; it is possible that the explanation for a given sex difference involves some of each. The only issue is whether the contribution of biology is greater than zero. I think that there are ten kinds of evidence that the contribution of biology is greater than zero, though of course it is nowhere near 100 percent. First, there are many biological mechanisms by which a sex difference could occur. There are large differences between males and females in levels of sex hormones, especially prenatally, in the first six months of life, and in adolescence. There are receptors for hormones all over the brain, including the cerebral cortex. There are many small differences in men’s and women’s brains, including the overall size of the brain (even correcting for body size), the density of cortical neurons, the degree of cortical asymmetry, the size of hypothalamic nuclei, and several others. Second, many of the major sex differences—certainly some of them, maybe all of them, are universal. The idea that there are cultures out there somewhere in which everything is the reverse of here turns out to be an academic legend. […] In personality, we have a cross-national survey (if not a true cross-cultural one) in Feingold’s metaanalysis, which noted that gender differences in personality are consistent across ages, years of data collection, educational levels, and nations. When it comes to spatial manipulation and mathematical reasoning, we have fewer relevant data, and we honestly don’t have true cross-cultural surveys, but we do have cross-national surveys. David Geary and Catherine Desoto found the expected sex difference in mental rotation in ten European countries and in Ghana, Turkey, and China. Similarly, Diane Halpern, analyzing results from ten countries, said that “the majority of the findings show amazing cross-cultural consistency when comparing males and females on cognitive tests.” Third, stability over time. Surveys of life interests and personality have shown little or no change in the two generations that have come of age since the second wave of feminism. There is also, famously, resistance to change in communities that, for various ideological reasons, were dedicated to stamping out sex differences, and found they were unable to do so. […] In tests of mental rotation, the meta-analysis by Voyer et al found no change over time. In mathematical reasoning there has been a decline in the size of the difference, although it has certainly not disappeared. Fourth, many sex differences can be seen in other mammals. It would be an amazing coincidence if these differences just happened to be replicated in the arbitrary choices made by human cultures at the dawn of time. There are large differences between males and females in many mammals in aggression, June 2015 WPE Reading Set C Page 8 of 23 in investment in offspring, in play aggression play versus play parenting, and in the range size, which predicts a species’ sex differences in spatial ability (such as in solving mazes), at least in polygynous species, which is how the human species is classified. Many primate species even show a sex difference in their interest in physical objects versus conspecifics, a difference seen their patterns of juvenile play. Among baby vervet monkeys, the males even prefer to play with trucks and the females with other kinds of toys! Fifth, many of these differences emerge in early childhood. […] Some sex differences seem to emerge even in the first week of life. […] A bit later in development there are vast and robust differences between boys and girls, seen all over the world. Boys far more often than girls engage in rough-and-tumble play, which involves aggression, physical activity, and competition. Girls spend a lot more often in cooperative play. Girls engage much more often in play parenting. And yes, boys the world over turn anything into a vehicle or a weapon, and girls turn anything into a doll. There are sex differences in intuitive psychology, that is, how well children can read one another’s minds. For instance, several large studies show that girls are better than boys in solving the “false belief task,” and in interpreting the mental states of characters in stories. Sixth, genetic boys brought up as girls. In a famous 1970s incident called the John/Joan case, one member of a pair of identical twin boys lost his penis in a botched circumcision [...]. Following advice from the leading gender expert of the time, the parents agreed to have the boy castrated, given femalespecific hormones, and brought up as a girl. All this was hidden from him throughout his childhood. […] When “Joan” and her family were interviewed years later, it turned out that from the youngest ages he exhibited boy-typical patterns of aggression and rough-and-tumble play, rejected girl-typical activities, and showed a greater interest in things than in people. At age 14, suffering from depression, his father finally told him the truth. He underwent further surgery, married a woman, adopted two children, and got a job in a slaughterhouse. This is not just a unique instance. In a condition called cloacal exstrophy, genetic boys are sometimes born without normal male genitalia. When they are castrated and brought up as girls, in 25 out of 25 documented instances they have felt that they were boys trapped in girls’ bodies, and showed malespecific patterns of behavior such as rough-and-tumble play. Seventh, a lack of differential treatment by parents and teachers. These conclusions come as a shock to many people. One comes from Lytton and Romney’s meta-analysis of sex-specific socialization involving 172 studies and 28,000 children, in which they looked both at parents’ reports and at direct observations of how parents treat their sons and daughters—and found few or no differences among contemporary Americans. In particular, there was no difference in the categories “Encouraging Achievement” and “Encouraging Achievement in Mathematics.” There is a widespread myth that teachers (who of course are disproportionately female) are dupes who perpetuate gender inequities by failing to call on girls in class, and who otherwise having low expectations of girls’ performance. In fact Jussim and Eccles, in a study of 100 teachers and 1,800 students, concluded that teachers seemed to be basing their perceptions of students on those students’ actual performances and motivation. Eighth, studies of prenatal sex hormones: the mechanism that makes boys boys and girls girls in the first place. There is evidence, admittedly squishy in parts, that differences in prenatal hormones make a difference in later thought and behavior even within a given sex. […] Ninth, circulating sex hormones. […] Though it’s possible that all claims of the effects of hormones on cognition will turn out to be bogus, I suspect something will be salvaged from this somewhat contradictory literature. There are, in any case, many studies showing that testosterone levels in the low-normal male range are associated with better abilities in spatial manipulation. And in a variety of studies in which estrogens are compared or manipulated, there is evidence, admittedly disputed, for June 2015 WPE Reading Set C Page 9 of 23 statistical changes in the strengths and weaknesses in women’s cognition during the menstrual cycle, possibly a counterpart to the changes in men’s abilities during their daily and seasonal cycles of testosterone. […] Are these stereotypes? Yes, many of them are (although, I must add, not all of them—for example, women’s superiority in spatial memory and mathematical calculation. There seems to be a widespread assumption that if a sex difference conforms to a stereotype, the difference must have been caused by the stereotype, via differential expectations for boys and for girls. But of course the causal arrow could go in either direction: stereotypes might reflect differences rather than cause them. In fact there’s an enormous literature in cognitive psychology which says that people can be good intuitive statisticians when forming categories and that their prototypes for conceptual categories track the statistics of the natural world pretty well. For example, there is a stereotype that basketball players are taller on average than jockeys. But that does not mean that basketball players grow tall, and jockeys shrink, because we expect them to have certain heights! Likewise, Alice Eagly and Jussim and Eccles have shown that most of people’s gender stereotypes are in fact pretty accurate. Indeed the error people make is in the direction of underpredicting sex differences. To sum up: I think there is more than “a shred of evidence” for sex differences that are relevant to statistical gender disparities in elite hard science departments. There are reliable average difference in life priorities, in an interest in people versus things, in risk-seeking, in spatial transformations, in mathematical reasoning, and in variability in these traits. And there are ten kinds of evidence that these differences are not completely explained by socialization and bias, although they surely are in part. A concluding remark. None of this provides grounds for ignoring the biases and barriers that do keep women out of science, as long as we keep in mind the distinction between fairness on the one hand and sameness on the other. And I will give the final word to Gloria Steinem: “there are very few jobs that actually require a penis or a vagina, and all the other jobs should be open to both sexes.” Elizabeth Spelke […] I want to start by talking about the points of agreement between Steve [Pinker] me, and as he suggested, there are many. If we got away from the topic of sex and science, we’d be hard pressed to find issues that we disagree on. Here are a few of the points of agreement that are particularly relevant to the discussions of the last few months. First, we agree that both our society in general and our university in particular will be healthiest if all opinions can be put on the table and debated on their merits. We also agree that claims concerning sex differences are empirical, they should be evaluated by evidence, and we’ll all be happier and live longer if we can undertake that evaluation as dispassionately and rationally as possible. We agree that the mind is not a blank slate; in fact one of the deepest things that Steve and I agree on is that there is such a thing as human nature, and it is a fascinating and exhilarating experience to study it. And finally, I think we agree that the role of scientists in society is rather modest. Scientists find things out. The much more difficult questions of how to use that information, live our lives, and structure our societies are not questions that science can answer. Those are questions that everybody must consider. So where do we disagree? We disagree on the answer to the question, why in the world are women scarce as hens’ teeth on Harvard’s mathematics faculty and other similar institutions? In the current debate, two classes of factors have been said to account for this difference. In one class are social forces, including overt and covert discrimination and social influences that lead men and women to develop different skills and different priorities. In the other class are genetic differences that predispose men and women to have different capacities and to want different things. June 2015 WPE Reading Set C Page 10 of 23 In his book, The Blank Slate, and again today, Steve argued that social forces are over-rated as causes of gender differences. Intrinsic differences in aptitude are a larger factor, and intrinsic differences in motives are the biggest factor of all. Most of the examples that Steve gave concerned what he takes to be biologically based differences in motives. My own view is different. I think the big forces causing this gap are social factors. There are no differences in overall intrinsic aptitude for science and mathematics between women and men. Notice that I am not saying the genders are indistinguishable, that men and women are alike in every way, or even that men and women have identical cognitive profiles. I’m saying that when you add up all the things that men are good at, and all the things that women are good at, there is no overall advantage for men that would put them at the top of the fields of math and science. On the issue of motives, I think we’re not in a position to know whether the different things that men and women often say they want stem only from social forces, or in part from intrinsic sex differences. I don’t think we can know that now. I want to start with the issue that’s clearly the biggest source of debate between Steve and me: the issue of differences in intrinsic aptitude. This is the only issue that my own work and professional knowledge bear on. Then I will turn to the social forces, as a lay person as it were, because I think they are exerting the biggest effects. Finally, I’ll consider the question of intrinsic motives, which I hope we’ll come back to in our discussion. […W]e’ve heard three arguments that men have greater cognitive aptitude for science. The first argument is that from birth, boys are interested in objects and mechanics, and girls are interested in people and emotions. The predisposition to figure out the mechanics of the world sets boys on a path that makes them more likely to become scientists or mathematicians. The second argument assumes, as Galileo told us, that science is conducted in the language of mathematics. On the second claim, males are intrinsically better at mathematical reasoning, including spatial reasoning. The third argument is that men show greater variability than women, and as a result there are more men at the extreme upper end of the ability distribution from which scientists and mathematicians are drawn. Let me take these claims one by one. The first claim, as Steve said, is gaining new currency from the work of Simon Baron-Cohen. It’s an old idea, presented with some new language. Baron-Cohen says that males are innately predisposed to learn about objects and mechanical relationships, and this sets them on a path to becoming what he calls “systematizers.” Females, on the other hand, are innately predisposed to learn about people and their emotions, and this puts them on a path to becoming “empathizers.” Since systematizing is at the heart of math and science, boys are more apt to develop the knowledge and skills that lead to math and science. To anyone as old as I am who has been following the literature on sex differences, this may seem like a surprising claim. The classic reference on the nature and development of sex differences is a book by Eleanor Maccoby and Carol Jacklin that came out in the 1970s. They reviewed evidence for all sorts of sex differences, across large numbers of studies, but they also concluded that certain ideas about differences between the genders were myths. At the top of their list of myths was the idea that males are primarily interested in objects and females are primarily interested in people. They reviewed an enormous literature, in which babies were presented with objects and people to see if they were more interested in one than the other. They concluded that there were no sex differences in these interests. Nevertheless, this conclusion was made in the early 70s. At that time, we didn’t know much about babies’ understanding of objects and people, or how their understanding grows. Since Baron-Cohen’s claims concern differential predispositions to learn about different kinds of things, you could argue that the claims hadn’t been tested in Maccoby and Jacklin’s time. What does research now show? June 2015 WPE Reading Set C Page 11 of 23 […] From birth, babies perceive objects. They know where one object ends and the next one begins. They can’t see objects as well as we can, but as they grow their object perception becomes richer and more differentiated. Babies also start with rudimentary abilities to represent that an object continues to exist when it’s out of view, and they hold onto those representations longer, and over more complicated kinds of changes, as they grow. Babies make basic inferences about object motion: inferences like, the force with which an object is hit determines the speed with which it moves. These inferences undergo regular developmental changes over the infancy period. In each of these cases, there is systematic developmental change, and there’s variability. Because of this variability, we can compare the abilities of male infants to females. Do we see sex differences? The research gives a clear answer to this question: We don’t. Male and female infants are equally interested in objects. Male and female infants make the same inferences about object motion, at the same time in development. They learn the same things about object mechanics at the same time. […] Common paths of learning continue through the preschool years, as kids start manipulating objects to see if they can get a rectangular block into a circular hole. If you look at the rates at which boys and girls figure these things out, you don’t find any differences. We see equal developmental paths. I think this research supports an important conclusion. In discussions of sex differences, we need to ask what’s common across the two sexes. One thing that’s common is infants don’t divide up the labor of understanding the world, with males focusing on mechanics and females focusing on emotions. Male and female infants are both interested in objects and in people, and they learn about both. The conclusions that Maccoby and Jacklin drew in the early 1970s are well supported by research since that time. Let me turn to the second claim. People may have equal abilities to develop intuitive understanding of the physical world, but formal math and science don’t build on these intuitions. Scientists use mathematics to come up with new characterizations of the world and new principles to explain its functioning. Maybe males have an edge in scientific reasoning because of their greater talent for mathematics. As Steve said, formal mathematics is not something we have evolved to do; it’s a recent accomplishment. Animals don’t do formal math or science, and neither did humans back in the Pleistocene. If there is a biological basis for our mathematical reasoning abilities, it must depend on systems that evolved for other purposes, but that we’ve been able to harness for the new purpose of representing and manipulating numbers and geometry. Research from the intersecting fields of cognitive neuroscience, neuropsychology, cognitive psychology, and cognitive development provide evidence for five “core systems” at the foundations of mathematical reasoning. The first is a system for representing small exact numbers of objects—the difference between one, two, and three. This system emerges in human infants at about five months of age, and it continues to be present in adults. The second is a system for discriminating large, approximate numerical magnitudes—the difference between a set of about ten things and a set of about 20 things. That system also emerges early in infancy, at four or five months, and continues to be present and functional in adults. The third system is probably the first uniquely human foundation for numerical abilities: the system of natural number concepts that we construct as children when we learn verbal counting. That construction takes place between about the ages of two and a half and four years. The last two systems are first seen in children when they navigate. One system represents the geometry of the surrounding layout. The other system represents landmark objects. June 2015 WPE Reading Set C Page 12 of 23 All five systems have been studied quite extensively in large numbers of male and female infants. We can ask, are there sex differences in the development of any of these systems at the foundations of mathematical thinking? Again, the answer is no. I will show you data from just two cases. The first is the development of natural number concepts, constructed by children between the ages of two and four. At any particular time in this period, you’ll find a lot of variability. […] When you compare children’s performance by sex, you see no hint of a superiority of males in constructing natural number concepts. The other example comes from studies that I think are the closest thing in preschool children to the mental rotation tests conducted with adults. In these studies, children are brought into a room of a given shape, something is hidden in a corner, and then their eyes are closed and they’re spun around. They have to remember the shape of the room, open their eyes, and figure out how to rotate themselves back to the object where it was hidden. If you test a group of 4 year olds, you find they can do this task well above chance but not perfectly; there’s a range of performance. When you break that performance down by gender, again there is not a hint of an advantage for boys over girls. These findings and others support two important points. First, indeed there is a biological foundation to mathematical and scientific reasoning. We are endowed with core knowledge systems that emerge prior to any formal instruction and that serve as a basis for mathematical thinking. Second, these systems develop equally in males and females. Ten years ago, the evolutionary psychologist and sex difference researcher, David Geary, reviewed the literature that was available at that time. He concluded that there were no sex differences in “primary abilities” underlying mathematics. What we’ve learned in the last ten years continues to support that conclusion. Sex differences do emerge at older ages. Because they emerge later in childhood, it’s hard to tease apart their biological and social sources. But before we attempt that task, let’s ask what the differences are. […] When people are presented with a complex task that can be solved through multiple different strategies, males and females sometimes differ in the strategy that they prefer. […]Because of these differences, males and females sometimes show differing cognitive profiles on timed tests. When you have to solve problems fast, some strategies will be faster than others. Thus, females perform better at some verbal, mathematical and spatial tasks, and males perform better at other verbal, mathematical, and spatial tasks. This pattern of differing profiles is not well captured by the generalization […] that women are “verbal” and men are “spatial.” There doesn’t seem to be any more evidence for that than there was for the idea that women are people-oriented and men are objectoriented. Rather the differences are more subtle. Does one of these two profiles foster better learning of math than the other? In particular, is the male profile better suited to high-level mathematical reasoning? At this point, we face a question that’s been much discussed in the literature on mathematics education and mathematical testing. The question is, by what yardstick can we decide whether men or women are better at math? Some people suggest that we look at performance on the SAT-M, the quantitative portion of the Scholastic Assessment Test. But this suggestion raises a problem of circularity. The SAT test is composed of many different types of items. Some of those items are solved better by females. Some are solved better by males. The people who make the test have to decide, how many items of each type to include? Depending on how they answer that question, they can create a test that makes women look like better mathematicians, or a test that makes men look like better mathematicians. What’s the right solution? Books are devoted to this question, with much debate, but there seems to be a consensus on one point: The only way to come up with a test that’s fair is to develop an independent understanding of what mathematical aptitude is and how it’s distributed between men and women. But in that case, we can’t June 2015 WPE Reading Set C Page 13 of 23 use performance on the SAT to give us that understanding. We’ve got to get that understanding in some other way. So how are we going to get it? […] Let me turn to the third claim, that men show greater variability, either in general or in quantitative abilities in particular, and so there are more men at the upper end of the ability distribution. […] As Steve said, students were screened at age 13 by the SAT, and there were many more boys than girls who scored at the highest levels on the SAT-M. In the 1980s, the disparity was almost 13 to 1. It is now substantially lower, but there still are more boys among the very small subset of people from this large, talented sample who scored at the very upper end. […] Fortunately, Benbow, Stanley and Lubinski have collected much more data on these mathematically talented boys and girls: not just the ones with top scores on one timed test, but rather the larger sample of girls and boys who were accelerated and followed over time. Let’s look at some of the key things that they found. First, they looked at college performance by the talented sample. They found that the males and females took equally demanding math classes and majored in math in equal numbers. More girls majored in biology and more boys in physics and engineering, but equal numbers of girls and boys majored in math. And they got equal grades. The SAT-M not only under-predicts the performance of college women in general, it also under-predicted the college performance of women in the talented sample. These women and men have been shown to be equally talented by the most meaningful measure we have: their ability to assimilate new, challenging material in demanding mathematics classes at top-flight institutions. By that measure, the study does not find any difference between highly talented girls and boys. So, what’s causing the gender imbalance on faculties of math and science? Not differences in intrinsic aptitude. Let’s turn to the social factors that I think are much more important. […] Let me start with studies of parents’ perceptions of their own children. […] Some studies have interviewed parents just after the birth of their child, at the point where the first question that 80% of parents ask—is it a boy or a girl?—has been answered. Parents of boys describe their babies as stronger, heartier, and bigger than parents of girls. The investigators also looked at the babies’ medical records and asked whether there really were differences between the boys and girls in weight, strength, or coordination. The boys and girls were indistinguishable in these respects, but the parents’ descriptions were different. At 12 months of age, girls and boys show equal abilities to walk, crawl, or clamber. But before one study, Karen Adolph, an investigator of infants’ locomotor development, asked parents to predict how well their child would do on a set of crawling tasks: Would the child be able to crawl down a sloping ramp? Parents of sons were more confident that their child would make it down the ramp than parents of daughters. When Adolph tested the infants on the ramp, there was no difference whatever between the sons and daughters, but there was a difference in the parents’ predictions. My third example, moving up in age, comes from the studies of Jackie Eccles. She asked parents of boys and girls in sixth grade, how talented do you think your child is in mathematics? Parents of sons were more likely to judge that their sons had talent than parents of daughters. A panoply of objective measures, including math grades in school, performance on standardized tests, teachers’ evaluations, and children’s expressed interest in math, revealed no differences between the girls and boys. Still, there was a difference in parents’ perception of their child’s intangible talent. Other studies have shown a similar effect for science. There’s clearly a mismatch between what parents perceive in their kids and what objective measures reveal. But is it possible that the parents are seeing something that the objective measures are missing? […] June 2015 WPE Reading Set C Page 14 of 23 A bunch of studies take the following form: you show a group of parents, or college undergraduates, video-clips of babies that they don’t know personally. For half of them you give the baby a male name, and for the other half you give the baby a female name. (Male and female babies don’t look very different.) The observers watch the baby and then are asked a series of questions: What is the baby doing? What is the baby feeling? How would you rate the baby on a dimension like strong-to-weak, or more intelligent to less intelligent? There are two important findings. First, when babies do something unambiguous, reports are not affected by the baby’s gender. If the baby clearly smiles, everybody says the baby is smiling or happy. Perception of children is not pure hallucination. Second, children often do things that are ambiguous, and parents face questions whose answers aren’t easily readable off their child’s overt behavior. In those cases, you see some interesting gender labeling effects. For example, in one study a child on a video-clip was playing with a jack-inthe-box. It suddenly popped up, and the child was startled and jumped backward. When people were asked, what’s the child feeling, those who were given a female label said, “she’s afraid.” But the ones given a male label said, “he’s angry.” Same child, same reaction, different interpretation. In other studies, children with male names were more likely to be rated as strong, intelligent, and active; those with female names were more likely to be rated as little, soft, and so forth. I think these perceptions matter. You, as a parent, may be completely committed to treating your male and female children equally. But no sane parents would treat a fearful child the same way they treat an angry child. If knowledge of a child’s gender affects adults’ perception of that child, then male and female children are going to elicit different reactions from the world, different patterns of encouragement. These perceptions matter, even in parents who are committed to treating sons and daughters alike. I will give you one last version of a gender-labeling study. This one hits particularly close to home. The subjects in the study were […] professors of psychology, who were sent some vitas to evaluate as applicants for a tenure track position. Two different vitas were used in the study. One was a vita of a walk-on-water candidate, best candidate you’ve ever seen, you would die to have this person on your faculty. The other vita was a middling, average vita among successful candidates. For half the professors, the name on the vita was male, for the other half the name was female. […] For the walk-on-water candidate, there was no effect of gender labeling on these judgments. I think this finding supports Steve’s view that we’re dealing with little overt discrimination at universities. It’s not as if professors see a female name on a vita and think, I don’t want her. When the vita’s great, everybody says great, let’s hire. What about the average successful vita, though: that is to say, the kind of vita that professors most often must evaluate? In that case, there were differences. The male was rated as having higher research productivity. These psychologists […] looked at the same number of publications and thought, “good productivity” when the name was male, and “less good productivity” when the name was female. Same thing for teaching experience. The very same list of courses was seen as good teaching experience when the name was male, and less good teaching experience when the name was female. In answer to the question would they hire the candidate, 70% said yes for the male, 45% for the female. If the decision were made by majority rule, the male would get hired and the female would not. A couple other interesting things came out of this study. The effects were every bit as strong among the female respondents as among the male respondents. Men are not the culprits here. […] So there’s a pervasive difference in perceptions, and I think the difference matters. Scientists’ perception of the quality of a candidate will influence the likelihood that the candidate will get a fellowship, a job, resources, or a promotion. A pattern of biased evaluation therefore will occur even in people who are absolutely committed to gender equity. […] These studies say that knowledge of a June 2015 WPE Reading Set C Page 15 of 23 person’s gender will influence our assessment of those factors, and that’s going to produce a pattern of discrimination, even in people with the best intentions. From the moment of birth to the moment of tenure, throughout this great developmental progression, there are unintentional but pervasive and important differences in the ways that males and females are perceived and evaluated. […] But the question on the table is not, Are there biological sex differences? The question is, Why are there fewer women mathematicians and scientists? The patterns of bias that I described provide four interconnected answers to that question. First, and most obviously, biased perceptions produce discrimination: When a group of equally qualified men and women are evaluated for jobs, more of the men will get those jobs if they are perceived to be more qualified. Second, if people are rational, more men than women will put themselves forward into the academic competition, because men will see that they’ve got a better chance for success. Academic jobs will be more attractive to men because they face better odds, will get more resources, and so forth. Third, biased perceptions earlier in life may well deter some female students from even attempting a career in science or mathematics. If your parents feel that you don’t have as much natural talent as someone else whose objective abilities are no better than yours, that may discourage you, as Eccles’s work shows. Finally, there’s likely to be a snowball effect. All of us have an easier time imagining ourselves in careers where there are other people like us. If the first three effects perpetuate a situation where there are few female scientists and mathematicians, young girls will be less likely to see math and science as a possible life. So by my personal scorecard, these are the major factors. Let me end, though, by asking, could Steve also be partly right? Could biological differences in motives—motivational patterns that evolved in the Pleistocene but that apply to us today—propel more men than women towards careers in mathematics and science? My feeling is that where we stand now, we cannot evaluate this claim. It may be true, but as long as the forces of discrimination and biased perceptions affect people so pervasively, we’ll never know. I think the only way we can find out is to do one more experiment. We should allow all of the evidence that men and women have equal cognitive capacity, to permeate through society. We should allow people to evaluate children in relation to their actual capacities, rather than one’s sense of what their capacities ought to be, given their gender. Then we can see, as those boys and girls grow up, whether different inner voices pull them in different directions. I don’t know what the findings of that experiment will be. But I do hope that some future generation of children gets to find out. Women in Academic Science: A Changing Landscape By Stephen J. Ceci, Donna K. Ginther, Shulamit Kahn, and Wendy M. Williams 3 Introduction We present a comprehensive synthesis of the empirical findings and logical analyses informing the question of why women are underrepresented in certain academic fields of science. Our emphasis is on those fields that are spatially and mathematically intensive—the ones in which women are most underrepresented—such as geoscience, engineering, economics, mathematics/computer science, and the physical sciences, including chemistry and physics (which we abbreviate as GEEMP). In this article, we compare math-intensive fields to non-math-intensive fields of science, including life science, psychology, and social science (which we abbreviate as LPS), in which women are often at parity with or exceed the number of men. A burgeoning literature bears on this topic, produced by scholars from diverse disciplines (e.g., psychology, economics, sociology, endocrinology, mathematics, philosophy, bibliometrics, and education), and there are many disagreements and confusions among researchers, the lay public, and policymakers. […] 3 Stephen J. Ceci and Wendy M. Williams teach in the Department of Human Development, Cornell University, Donna K. Ginther teaches in Department of Economics, University of Kansas, and Shulamit Kahn teaches at the Questrom School of Business, Boston University. June 2015 WPE Reading Set C Page 16 of 23 There has been a tendency in the literature to conflate historical findings with current findings, thus obscuring both trends over time and the current state of the field. (This is particularly likely to happen when discussing hiring, persistence, and remuneration rates that may have changed in recent decades or even in recent years.) In fact, the results we present below show such a dramatic increase in the number of women in science at all levels over the past 40 years that research based on data prior to the 1990s may have little bearing on the current circumstances women encounter. As a result, we present the most recent data available, and our synthesis of the literature emphasizes recent studies—those done since 2000—augmented by our own analyses of recent data in order to shed light on the current situation for women in science, rather than on what was once historically true.[…] An Overview of the Problem “Contradictory” is the word that best characterizes the literature on women’s underrepresentation in academic science. There is agreement that women are underrepresented in all math-intensive fields in the academy. In all GEEMP fields in 2010, for example, women comprised only 25% to 44% of tenure-track assistant professors and 7% to 16% of full professors […]. But there is heated debate over why women are so conspicuously absent in these fields compared with LPS fields. In the LPS fields, the comparable figures show that women hold 66% of the tenure-track assistant professorships in psychology, 45% in social science (excluding economics), and 38% in life science; for full professorships, the figures are 35%, 23%, and 24%, respectively. […] Educational milestones for women and men Half of all 24to 25-year-olds with at least a high school diploma are women, but women have represented more than half of bachelor’s-degree recipients since the mid-1980s, and made up 57% of bachelor’s-degree holders as of 2010 (Fig. 1a). [… W]omen are equally represented or overrepresented in some STEM fields (the LPS fields) and underrepresented in others (the GEEMP fields). […B]y 2011, the proportion of females among the bachelor’sdegree-holding STEM majors was only a few percentage points below the proportion of females among all majors (averaging 6.5 percentage points), and was essentially the same as the proportion of females among high school graduates— with all exceeding 50%. However, the contrast between GEEMP and LPS fields is stark. Women received only 25% of GEEMP bachelor’s degrees in 2011, a more-than-30-percentage-point difference from the overall percentage of females among bachelor’s-degree recipients. Moreover, after growing in the 1990s, the percentage of women in these majors has become increasingly smaller since 2002. In contrast, women are significantly overrepresented in LPS fields, receiving almost 70% of these bachelor’s degrees. As in the GEEMP fields, the number of female baccalaureates in LPS fields grew in the 1990s; however, it has not fallen during the past decade. Thus, combining all STEM fields masks important differences in degree trends between GEEMP and LPS fields. […] Evidence on Potential Explanations for Women’s Underrepresentation in Academic GEEMP Careers Although women’s underrepresentation in math-intensive fields is not in doubt, its cause is hotly disputed. The disciplines of economics and psychology differ in their approach to and modeling of gender differences in career outcomes. In general, economists focus on comparative advantage, whereby individuals choose to work in areas where they are relatively more productive—weighing the costs and benefits of alternative careers and nonmarket activities—and on market forces balancing supply (based on comparative advantage) and demand (based on productivity); when these explanations are not supported by the data, economists try to understand why discrimination can be a self-reinforcing equilibrium. In contrast, psychology has tended to focus on early socialization practices, implicit and explicit biases, stereotypes, and biological sex differences in explaining this gap. In the face of the evidence just shown—indicating that the sources of the underrepresentation in GEEMP fields can be seen early, many years before college—the economists among us agree with the psychologists that early socialization and possibly even biological differences can lead to differences in comparative advantage. Moreover, they emphasize that anticipated gender differences in future career opportunities lead to behaviors and choices that reinforce early socialization, a position that psychologists also endorse. And the psychologists among us agree with the economists that productivity differences play an important role in explaining later persistence, promotion, and salary in some fields, as we describe later. June 2015 WPE Reading Set C Page 17 of 23 In LPS fields, the issues are different. Women drop off the academic ladder post-bachelor’s. Here, too, the economists and psychologists on our team started by emphasizing different possible avenues of postbaccalaureate gender differences, with economists emphasizing rational choices, where the opportunity cost of balancing work and family is associated with not pursuing academic science, and psychologists emphasizing people versus-things preferences that result in many STEM females opting for medicine, law, and veterinary science over GEEMP fields. Psychologists have charted large sex differences in occupational interests, with women preferring so-called “people-oriented” (or “organic,” or natural science) fields and men preferring “things” (people and thing-oriented individuals are also termed “empathizers” and “systematizers,” respectively […]. This people-versus things construct goes back to Thorndike (1911) and is one of the salient dimensions running through vocational interests; it also represents a difference of 1 standard deviation between men and women in vocational interests. Lippa has repeatedly documented very large sex differences in occupational interests, including in transnational surveys, with men more interested in “thing”-oriented activities and occupations, such as engineering and mechanics, and women more interested in people-oriented occupations, such as nursing, counseling, and elementary school teaching […]. And in a very extensive meta-analysis of over half a million people, Su, Rounds, and Armstrong (2009) reported a sex difference on this dimension of a full standard deviation.6 However, despite differences between us at the start, over time our respective views on women’s migration from LPS appear to have converged […] Conclusions: Refocusing Today’s Debate on Women in Science We began this article by noting how rapidly women have increased their representation in all STEM disciplines. In the 1970s, women received less than half of all STEM degrees. Since that time, progress has been uneven. In the case of LPS fields, women have attained a critical mass, sometimes comprising over 50% of assistant professors and a quarter to a third of full professors. In contrast, women are in shorter supply in GEEMP fields. Women comprised about 10% of bachelor’s-degree recipients in these fields (in engineering, only 1%) in the 1970s and are now receiving between 20% and 40% of bachelor’s degrees. The most recent figures indicate that in these fields, women comprise only 25% to 44% of tenure-track assistant professors and only 7% to 16% of full professors. The goal of this article was to explore and explain the basis of this difference. Myriad causes have been alleged to explain women’s underrepresentation in GEEMP disciplines—chilly climate, biased interviewing and hiring, lack of female role models, lack of mentors, biased tenure and promotion, unfair salary, sex differences in quantitative and spatial abilities, lower productivity and impact, stereotype threat, and sex differences in career preferences. So what explanation (or explanations) do the data support? […] We begin with a seeming paradox. The fields in which women are in shortest supply are (with the exception of economics) the very fields in which they appear to have the most success at being hired, promoted, and remunerated as professors. Recall that it is in GEEMP fields (often with the exception of economics) that women and men proceed from college major to PhD in equal proportions, in which women applicants are invited to interview and are hired at higher rates than their male colleagues, and in which women are tenured, promoted, and remunerated comparably to their male colleagues. And it is in GEEMP fields that women persist in their careers as long as their male peers—with the occasional exception. But we want to underscore that the exceptions are occasional, and that the overall picture is one of gender neutrality in GEEMP fields, notwithstanding frequent claims to the contrary. Thus, the paradox: Why are women in shortest supply in the very fields in which they appear to fare best? Our analyses and research synthesis led us to dismiss as important causes of women’s underrepresentation pay or citations per published article, both of which were equivalent (with some exceptions) for the two sexes. Our analysis also ruled out discriminatory grant and journal reviewing and biased hiring and promotion decisions, none of which have been consistently demonstrated to occur. These factors are not related to women’s underrepresentation in academic science careers, which has led us to the conclusion that the overall state of the academy is largely one of gender neutrality. There are some important ways in which women and men differ, and some of these may be related to differential outcomes by sex. For example, our research shows that women on average publish fewer papers than men (although their citations per published paper are the same). Given the central importance of publications to the progression of academic careers—in fact, of all variables, it is publications that are often argued to be the single most important measure of academic success—women’s lower productivity in publishing may be seen as a key variable in some differential outcomes. Furthermore, there is no evidence that women’s relatively fewer publications are higher in quality and impact. Better data on individual publications and citations, and more research in general, is required to determine whether differences in productivity (quantity and quality) account for some of the observed differences in salaries and promotion. June 2015 WPE Reading Set C Page 18 of 23 Given that the factors just discussed do not explain the gender gap in math-intensive GEEMP fields, what does? This long list of exclusions still leaves occupational preferences (women are more likely to prefer organic fields that involve living things, whereas men tend to prefer fields that emphasize symbol manipulation), and the roots of these differences can be seen in the type of AP coursework that high school students take as well as in their choice of college majors. Also, the list of potential causes still leaves open the key issue of the impact of children. However, why would the presence of children reduce a woman’s likelihood of entering GEEMP fields and not, say, life science, in which the temporal inflexibility of lab work would seem to be at least as great? Ginther and Kahn (2009) found that family variables did not dissuade women from pursuing academic careers in the physical sciences and engineering. But that study was based on data through 2001, and given the dramatic changes we have documented here, it is entirely possible that as more women have entered GEEMP fields, family variables may now have begun to influence career decisions. As we have suggested elsewhere (W. M. Williams & Ceci, 2012), childrearing may have adverse effects on women’s careers in all fields, but these effects might be more apparent in GEEMP fields because women’s numbers are relatively low to begin with. That is, if the plan to have children dissuades some female PhD students or postdocs from entering the competition for tenure-track jobs in psychology or biology (the two fields in which the transition showed the largest loss of females), this effect will be less apparent because women currently constitute 40% to 67% of assistant professors in these fields. In contrast, the reduction of women resulting from a similar decision not to enter tenure-track job competition in engineering will exacerbate an already-existing dearth of women. Thus, the negative impact of children on the work lives of women is foregrounded in fields in which they are relatively sparse to begin with. This still leaves as potential causes of underrepresentation sex differences in occupational preferences that are evident by middle school and that result in different high school course choices (i.e., accelerated and AP coursework in physics, Calculus BC, and chemistry). Females profess to be more interested in medicine, biology, law, psychology, and veterinary medicine from a relatively young age, whereas males are more likely to prefer engineering and computer science. Perhaps it should not be surprising to discover that the sexes trod divergent paths after all. Our review of the evidence leaves open one possibility raised recently by Goldin (2014) in her presidential address to the American Economic Association. Her analysis revealed the premium that women disproportionately place on flexible work conditions, which in turn results in lower wages and promotion, but only in some fields. Her analyses, model, and arguments suggest that women’s status in science is the result of personal choices and time-flexibility preferences, as opposed to sex differences in human capital and sex-based salary discrimination (except insofar as one can argue that companies are implicitly biased for not making flexibility available at a lower cost to those desiring it). Her analyses explain why sex differences in salaries usually begin very small, grow over time, and manifest nonlinearly (with hours worked) in some fields but not in others. Goldin has shown that if women place a premium on flexibility, then purchasing that amenity can be quite costly in some fields because of the nature of the work and the size of typical workplaces (e.g., for MBAs in finance or Juris Doctors), but not in others (e.g., in pharmacy or large practices in which workers are interchangeable). Thus, she has brought personal choice, a factor that has been derided by some gender-equity advocates, back into the picture. In a related vein, Ferriman et al. (2009) showed that men and women from top graduate programs in STEM fields expressed quite similar preferences for flexibility in their future work schedules and for working less than 50 (or 60) hours per week while they were still in graduate school, but by their mid-30s, the women with children were much less like men (with or without children) and women without children with respect to preferring to avoid long hours and wanting flexible schedules. Further, Lubinski and Benbow (2006) found in both this same sample of ex-graduate students as well as a sample of people who had been exceptionally able in mathematics as adolescents (also in their mid-30s) that women not only were more likely to prefer to work fewer hours but actually did so. Relatedly, in graduate school, both sexes actually reported devoting 20 hours a week to studying and 30 hours a week to research: no sex differences (see Lubinski et al., 2001, Table 3, p. 315). Time flexibility is thus a priority for at least a portion of women who excel in science. And, as we argued earlier, it seems that these women often eschew faculty jobs in order to accommodate these preferences. Looking at current statistics (from the SDR), we found that women with LPS PhDs were the ones least likely to pursue tenure-track positions and most likely to have shorter hours in their work outside tenure-track academia. […] June 2015 WPE Reading Set C Page 19 of 23 Another way in which our review of the evidence is limited is by what is not available in the scientific literature and by the adequacy of available evidence. At a number of junctures we presented survey data and anecdotal claims because they were the only evidence available. In discussing sex differences in job satisfaction and climate, for example, we presented claims by women from surveys or interviews stating that they were on average less satisfied with their work climate or jobs than men. However, the meaning of such self-reports is unclear. Is women’s self-reported lower level of satisfaction due to salary and promotion factors, or is women’s relatively lower reported level of satisfaction due to men’s socialization inhibiting them from readily disclosing (or even acknowledging) unhappiness with their jobs, potentially because such acknowledgement signals weakness (men have been shown to underreport pain, for example) […] That is, is women’s lower satisfaction due to their greater willingness to label themselves as dissatisfied? And what are the measurable outcomes of this gap in satisfaction between men and women (when it exists, given that it is not consistent)? We do not know. […] In sum, depending on the life-course transition point, the cause of early lack of interest in GEEMP subjects and later attrition from GEEMP fields is the result of one or more of a confluence of variables. Attempts to reduce these causes to a single “culprit” (e.g., bias by search committees against female applicants; women’s preference for other fields or lack of math aptitude; publication rates; salary differentials) are not supported by the full corpus of data and research findings. Granted, one can cherry-pick aberrant examples that seem to suggest bias or aptitude gaps or differences between the sexes in productivity or impact, but the entire scientific corpus reveals that no single cause can account for the dearth of women in GEEMP careers. The most significant implication of our analysis is that failure to acknowledge the nature, complexity, and timing of causes limits progress in increasing women’s representation in math-intensive careers, by directing resources to areas that are not currently major reasons for the dearth of women in math-intensive fields. It is our hope that we have helped move the debate from slogans and rallying cries to a judicious consideration of the full corpus of scientific data. This may make it harder to make sweeping indictments, but that is a price worth paying for scientific accuracy. […] Science faculty’s subtle gender biases favor male students By Corinne A. Moss-Racusina, John F. Dovidio, Victoria L. Brescoll, Mark J. Grahama, and Jo Handelsmana 4 Edited by Shirley Tilghman, Princeton University, Princeton, NJ […] Whether these gender biases operate in academic sciences remains an open question. On the one hand, although considerable research demonstrates gender bias in a variety of other domains, science faculty members may not exhibit this bias because they have been rigorously trained to be objective. On the other hand, research demonstrates that people who value their objectivity and fairness are paradoxically particularly likely to fall prey to biases, in part because they are not on guard against subtle bias. Thus, by investigating whether science faculty exhibit a bias that could contribute to the gender disparity within the ?elds of science, technology, engineering, and mathematics (in which objectivity is emphasized), the current study addressed critical theoretical and practical gaps in that it provided an experimental test of faculty discrimination against female students within academic science. [… Current Study In addition to determining whether faculty expressed a bias against female students, we also sought to identify the processes contributing to this bias. To do so, we investigated whether faculty members’ perceptions of student competence would help to explain why they would be less likely to hire a female (relative to an identical male) student for a laboratory manager position. Additionally, we examined the role of faculty members’ preexisting subtle bias against women. We reasoned that pervasive cultural messages regarding women’s lack of competence in science could lead faculty members to hold gender-biased attitudes that might subtly affect their support for female (but not 4 Corinne A. Moss-Racusina (Department of Molecular, Cellular and Developmental Biology and of Psychology), John F. Dovidio (Department of Psychology), Victoria L. Brescoll (School of Management), and Mark J. Graham (Department of Molecular, Cellular and Developmental Biology and of Psychiatry) teach at Yale University. Jo Handelsman currently serves as the Associate Director for Science at the White House office of Science and Technology Policy. June 2015 WPE Reading Set C Page 20 of 23 male) science students. These generalized, subtly biased attitudes toward women could impel faculty to judge equivalent students differently as a function of their gender. The present study sought to test for differences in faculty perceptions and treatment of equally quali?ed men and women pursuing careers in science and, if such a bias were discovered, reveal its mechanisms and consequences within academic science. We focused on hiring for a laboratory manager position as the primary dependent variable of interest because it functions as a professional launching pad for subsequent opportunities. As secondary measures, which are related to hiring, we assessed: (i) perceived student competence; (ii ) salary offers, which re?ect the extent to which a student is valued for these competitive positions; and (iii ) the extent to which the student was viewed as deserving of faculty mentoring. Our hypotheses were that: Science faculty’s perceptions and treatment of students would reveal a gender bias favoring male students in perceptions of competence and hireability, salary conferral, and willingness to mentor (hypothesis A); Faculty gender would not in?uence this gender bias (hypothesis B); Hiring discrimination against the female student would be mediated (i.e., explained) by faculty perceptions that a female student is less competent than an identical male student (hypothesis C); and Participants’ preexisting subtle bias against women would moderate (i.e., impact) results, such that subtle bias against women would be negatively related to evaluations of the female student, but unrelated to evaluations of the male student (hypothesis D). Results A broad, nationwide sample of biology, chemistry, and physics professors (n = 127) evaluated the application materials of an undergraduate science student who had ostensibly applied for a science laboratory manager position. All participants received the same materials, which were randomly assigned either the name of a male (n = 63) or a female (n = 64) student; student gender was thus the only variable that differed between conditions. Using previously validated scales, participants rated the student’s competence and hireability, as well as the amount of salary and amount of mentoring they would offer the student. Faculty participants believed that their feedback would be shared with the student they had rated. Student Gender Differences. […]In each case, the effect of student gender was signi?cant (all P < 0.01), whereas the effect of faculty participant gender and their interaction was not […]. Tests of simple effects […] indicated that faculty participants viewed the female student as less competent […] and less hireable […] than the identical male student. Faculty participants also offered less career mentoring to the female student than to the male student […]. The mean starting salary offered the female student, $26,507.94, was signi?cantly lower than that of $30,238.10 to the male student […]. These results support hypothesis A. In support of hypothesis B, faculty gender did not affect bias. Tests of simple effects […] indicated that female faculty participants did not rate the female student as more competent […] or hireable […] than did male faculty. Female faculty also did not offer more mentoring […] or a higher salary […] to the female student than did their male colleagues. In addition, faculty participants’ scienti?c ?eld, age, and tenure status had no effect […]. Thus, the bias appears pervasive among faculty and is not limited to a certain demographic subgroup. Mediation and Moderation Analyses. [...] Evidence . emerged for hypothesis C, the predicted mediation (i.e., causal path). […] This pattern of results provides evidence for full mediation, indicating that the female student was less likely to be hired than the identical male because she was viewed as less competent overall. We also conducted moderation analysis (i.e., testing for factors that could amplify or attenuate the demonstrated effect) to determine the impact of faculty participants’ preexisting subtle bias against women on faculty participants’ perceptions and treatment of male and female science students. For this purpose, we administered the Modern Sexism Scale, a well-validated instrument frequently used for June 2015 WPE Reading Set C Page 21 of 23 this purpose. Consistent with our intentions, this scale measures unintentional negativity toward women, as contrasted with a more blatant form of conscious hostility toward women. Results of multiple regression analyses indicated that participants’ preexisting subtle bias against women signi?cantly interacted with student gender to predict perceptions of student composite competence […], hireability […], and mentoring […]. To interpret these signi?cant interactions, we examined the simple effects separately by student gender. Results revealed that the more preexisting subtle bias participants exhibited against women, the less composite competence […] and hireability […] they perceived in the female student, and the less mentoring […] they were willing to offer her. In contrast, faculty participants’ levels of preexisting subtle bias against women were unrelated to the perceptions of the male student’s composite competence […] and hireability […], and the amount of mentoring […] they were willing to offer him. […] Thus, it appears that faculty participants’ preexisting subtle gender bias undermined support for the female student but was unrelated to perceptions and treatment of the male student. These ?ndings support hypothesis D. Finally, using a previously validated scale, we also measured how much faculty participants liked the student. In keeping with a large body of literature, faculty participants reported liking the female (mean = 4.35, SD = 0.93) more than the male student [(mean = 3.91, SD = 0.1.08), t(125) = −2.44, P < 0.05]. However, consistent with this previous literature, liking the female student more than the male student did not translate into positive perceptions of her composite competence or material outcomes in the form of a job offer, an equitable salary, or valuable career mentoring. Moreover, only composite competence (and not likeability) helped to explain why the female student was less likely to be hired; in mediation analyses, student gender condition (β = −0.48, P

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