Selbermach Samstag 327 (13.02.2021)

Welche Themen interessieren euch, welche Studien fandet ihr besonders interessant in der Woche, welche Neuigkeiten gibt es, die interessant für eine Diskussion wären und was beschäftigt euch gerade?

Welche interessanten Artikel gibt es auf euren Blogs? (Schamlose Eigenwerbung ist gerne gesehen!)

Welche Artikel fandet ihr in anderen Blogs besonders lesenswert?

Welches Thema sollte noch im Blog diskutiert werden?

Für das Flüchtlingsthema oder für Israel etc gibt es andere Blogs

Ich erinnere auch noch mal an Alles Evolution auf Twitter und auf Facebook.

Wer mal einen Gastartikel schreiben möchte, auch gerne einen feministischen oder sonst zu hier geäußerten Ansichten kritischen, der ist dazu herzlich eingeladen

Es wäre nett, wenn ihr Artikel auf den sozialen Netzwerken verbreiten würdet.

Gründe für Geschlechterunterschiede in STEM: Vorurteile und Diskriminierung am Arbeitsplatz (Teil 4)

Eine interessante Studie bespricht eine Vielzahl von Gründen, warum sich Geschlechterunterschiede im STEM-Bereich ergeben.

It is a well-known and widely lamented fact that men outnumber women in a number of fields in STEM (science, technology, engineering and maths). The most commonly discussed explanations for the gender gaps are discrimination and socialization, and the most common policy prescriptions target those ostensible causes. However, a great deal of evidence in the behavioural sciences suggests that discrimination and socialization are only part of the story. The purpose of this paper is to highlight other aspects of the story: aspects that are commonly overlooked or downplayed. More precisely, the paper has two main aims. The first is to examine the evidence that factors other than workplace discrimination contribute to the gender gaps in STEM. These include relatively large average sex differences in career and lifestyle preferences, and relatively small average differences in cognitive aptitudes – some favouring males, others favouring females – which are associated with progressively larger differences the further above the average one looks. The second aim is to examine the evidence suggesting that these sex differences are not purely a product of social factors but also have a substantial biological (i.e. inherited) component. A more complete picture of the causes of the unequal sex ratios in STEM may productively inform policy discussions.

Quelle: Men, women and STEM: Why the differences and what should be done?

Die Einteilung in der Studie ist wie folgt:

  1. Sex differences in preferences and priorities
  2. Sex differences in cognitive aptitudes
  3. Sex differences in variability
  4. Bias and discrimination in the workplace
  5. Policy implications
  6. Levelling the playing field vs. equalizing sex ratios
  7. Conclusion: Many factors at play

Ich dachte ich gehe diese Punkte mal einzeln durch, weil da viel interessantes dabei ist

Heute also:

Vorurteile und Diskriminierung am Arbeitsplatz

Los geht es:


Es könnte also – neben den anderen Faktoren – auch noch eine Diskrimierung abschrecken. Im Feminismus ist das der einzige Grund, andere darf es gar nicht geben. Und dann wird noch nicht einmal wirklich aufgeschlüsselt, welche Faktoren genau vorliegen. Es ist einfach Diskriminierung. Und die Männer müssen sich ändern.

The discrimination hypothesis for STEM gender gaps is clearly worth taking seriously; after all, no one denies that there was considerable discrimination in STEM prior to the second wave of the feminist revolution, and it may be unduly optimistic to think that this would evaporate completely in little more than half-a-century. At the same time, though, the hypothesis constitutes a rather serious accusation against people working in STEM and a rather serious indictment of existing institutions. As such, it is only fair to look carefully at the evidence for and against the hypothesis. For the reasons given already, gender disparities are not in themselves direct evidence of discrimination; unless the sexes were psychologically identical, equality of opportunity would almost certainly not translate into equality of outcomes (Steven Pinker, 2002Radcliffe-Richards, 2014). Nonetheless, various lines of evidence do bear on the question of how much discrimination remains in the world of STEM. In the following, we survey the evidence for discrimination against women in STEM, then look at evidence that complicates the picture – including evidence suggesting discrimination against men.

Das sollte der Aufbau einer Studie


Bias and discrimination against women in STEM

The most abundant source of evidence for bias against women in STEM comes from experimental studies looking at people’s reactions to hypothetical applicants for STEM jobs. Otherwise identical job applications are given either a female or a male name, and then evaluated by participants naïve to the purpose of the study. In one widely cited paper in this genre, Moss-Racusin et al. (2012) had science faculty from six major universities rate applications for a laboratory manager position. They found that the raters – female and male alike – gave higher ratings to supposedly male applicants than they did to supposedly female ones. Specifically, participants rated the males as more competent and hireable, and as deserving a higher salary. In another, earlier study, Steinpreis et al. (1999) found that, for middling job applications, academic psychologists – again, female and male alike – expressed greater willingness to hire a male job candidate than an identical female one. For outstanding applications, on the other hand, there was no effect of gender.

Admittedly, both of these studies had a number of weaknesses, including the fact that the samples included only 127 and 238 participants, respectively. However, the findings are broadly consistent with a large body of research in the area. A meta-analysis of studies looking at simulated employment decisions (N = 22,348) found little or no gender bias in female-dominated or gender-balanced fields, but a small-to-moderate pro-male bias among males in male-dominated fields (d = 0.3; Koch et al., 2015, Table 2). Furthermore, although participants in male-dominated areas exhibited little gender bias when judging candidates with unambiguously positive or negative traits, they did exhibit a pro-male bias when judging participants with average traits or a mixture of positive and negative (Koch et al., 2015, Table 3). The meta-analysis did not focus specifically on STEM-related hiring decisions; however, given that many STEM fields are male-dominated, the results nonetheless increase the plausibility of the STEM-specific findings.

Also nicht die absolute Diskriminerung, wie sie der Feminismus anführt, sondern geringfügige Vorurteile.

Of course, even if the results are valid, it is not clear whether they generalize to real-world hiring decisions, where decision makers have more experience and are more motivated to make the best decision. Indeed, the Koch et al. meta-analysis showed that pro-male biases were effectively eliminated in those circumstances (d = 0.01).

Profis in der realen Welt hatten also in der gleichen Studie nahezu keine Vorurteile.

Furthermore, as we discuss later, other research suggests that the hiring bias in STEM may sometimes go the other way (Ceci et al., 2014Williams & Ceci, 2015). Still, it is entirely possible that anti-female bias plays a role in STEM hiring decisions, at least in some cases.

Von  Studien, die eine Bevorzugung von Frauen aufzeigen´, würde man auf der anderen Seite wohl kaum etwas hören. Das passt nicht zur Opferrolle.

Moreover, hiring is not the only domain in which discrimination could occur. Other studies have uncovered other possible examples of discrimination, in STEM and in academia more broadly. Among the best conducted and most persuasive studies are the following:

  • A study of Israeli primary schools found that boys got higher marks in maths assessments where the students‘ gender was known than in gender-blind ones, whereas girls got higher marks in the gender-blind assessments. In other words, maths teachers tended to favour boys when assessing students’ maths abilities. Teacher favouritism was associated with greater subsequent maths achievement among boys, and a greater likelihood of enrolling in advanced maths classes in high school (Lavy & Sand, 2018).

Hier die Studie:

We estimate the effect of primary school teachers‘ gender biases on boys‘ and girls‘ academic achievements during middle and high school and on the choice of advanced level courses in math and sciences during high school in Tel-Aviv, Israel. We measure bias using class-gender differences in scores between school exams graded by teachers and national exams graded blindly by external examiners. For identification, we rely on the random assignment of teachers and students to classes in primary schools. Our results suggest that assignment to a teacher with a greater bias in favor of girls (boys) has positive effects on girls‘ (boys‘) achievements. Such gender biases have also positive impact on girls‘ (boys‘) enrollment in advanced level math courses in high school. These results suggest that teachers‘ biased behavior at early stages of schooling has long run implications for occupational choices and earnings at adulthood, because enrollment in advanced courses in math and science in high school is a prerequisite for post-secondary schooling in engineering, computer science and so on.


      • Professors in the US are less likely to respond to informal inquiries about a PhD programme when the inquirer is a woman (Milkman et al., 2015).
      • In 2018, several Japanese medical schools admitted favouring male applicants to their programmes (Cyranoski, 2018).
      • In several online samples, people were more likely to refer a man than a woman for a hypothetical job when the job was described as requiring extreme intellectual ability (Bian et al., 2018).
      • In a large audit study (in which fictitious job applications are sent out in response to genuine job advertisements, and subsequent call-backs counted), high-achieving men received twice as many call-backs as high-achieving women – and three times as many among maths majors (Quadlin, 2018).
      • Economics papers authored by women need to be better written to be accepted into top-tier journals (Hengel, 2017).
      • Neuroscience papers with a male first author and male last author are more likely to be cited than those with first and last authors of different sexes, or those with a female first author and female last author (Dworkin et al., 2020). This is driven largely by men’s citation practices.
      • Male researchers in animal psychology and social cognition are more likely to share their data and published research with other men than with women (Massen et al., 2017).
      • According to one major meta-analysis, men have a 7% better chance of being awarded research grants (Bornmann et al., 2007).
      • Female academics less often give talks at prestigious US universities, even controlling for the rank of the available speakers, and even though women are apparently no more likely to turn down an invitation (Nittrouer et al., 2018).
      • Women commonly get lower ratings than men in teaching evaluations (Rosen, 2018), even in experimental studies that equalize teaching quality (MacNell et al., 2015Mengel et al., 2018).
      • Women may encounter sexism or harassment at work, in the field or at conferences, which may contribute to a desire to leave STEM or academia (Biggs et al., 2018Clancy et al., 2014Funk & Parker, 2018).


      One might point to weaknesses in any particular study, or worry about a general tendency to seek, report and cite only results that confirm a narrative of female disadvantage and male privilege (Duarte et al., 2015Honeycutt & Jussim, 2020Seager & Barry, 2019). Still, the sheer number of studies finding anti-female bias makes it difficult to maintain that there is no bias at all, even if the level of bias might sometimes be overstated.

      Also durchaus einige Studien, die Nachteile für Frauen belegen, wenn auch üblicherweise kleinere.

      Challenges to the discrimination explanation for STEM gender gaps

      At the same time, a number of cautions and qualifications are necessary. First, it is important to emphasize that workplace discrimination is almost certainly not the whole story when it comes to STEM gender gaps. As discussed, sex differences in preferences and cognitive specializations are well-documented, and regardless of the ultimate causes of these differences, it is unrealistic to imagine that they have no effect on people’s occupational outcomes.

      Furthermore, discrimination alone cannot readily explain why women are less well represented in some fields than others. Why would discrimination stop women from going into fields such as physics and engineering, but not into other prestigious, high-paying fields such as law, medicine or veterinary science? One suggestion might be that the former fields are particularly inhospitable to women as a result of the stereotype that women lack the mathematical ability or intellectual brilliance to succeed in these domains. The problem with this idea, though, is that when universities first began opening their doors to women, many people thought that women lacked the intellectual ability to succeed in any academic field (Boddice, 2011Clabaugh, 2010). Despite that, women were able to reach parity with men, or even surpass them, in virtually every other area. Why would discrimination only hold women back in maths-intensive fields or fields currently assumed to require intellectual brilliance? And why would it hold them back in the same fields everywhere in the world, rather than, say, maths-intensive fields in the United States, psychology in South Africa and law in Scandinavia? Bias and discrimination fail to explain why women are consistently underrepresented in some fields but not in others. In contrast, sex differences in interests and cognitive specializations provide a straightforward explanation for the pattern.

      In der Tat sind bestimmte Felder, die ebenso Vorurteilsbehaftet waren, schnell von Frauen erobert worden. Andere Felder wiedersetzen sich energisch dem aufholen.

      Not only does discrimination fail to explain major trends in the data, but the evidence for discrimination in STEM is considerably more mixed than is often assumed. Certainly, as we have just seen, many studies have found evidence of anti-female discrimination in STEM. At the same time, however, many other studies have failed to find such discrimination, or have found discrimination in favour of women. This raises the possibility that our picture of the level and nature of discrimination in STEM is somewhat distorted.

      The most important voices on this topic are Stephen Ceci and Wendy Williams (2011). In their view, the idea that women are routinely discriminated against in STEM, while true in earlier generations, is no longer true. The culture of STEM has changed a great deal over the last half century, but people’s beliefs about that culture have not kept pace with the change.

      Also die Idee, dass inzwischen ein Wechsel eingetreten ist und die Diskriminierung inzwischen gerade nicht mehr vorliegt.

      Take hiring decisions. Real-world data going back to the 1980s suggest that, although fewer women apply for jobs in fields such as maths, physics, chemistry, biology and engineering, those who do apply are no less likely to be interviewed and no less likely to be offered the job. On the contrary, they are generally more likely to be (Ceci, 2018).6 This is the opposite of what we would expect if there were pervasive anti-female bias in STEM. If anything, it looks like there may be a pro-female bias, at least in the modern West.7

      Aus der Studie:

      Although women are underrepresented in the most mathematically intensive fields, the gender gap in these fields has narrowed over the past 2 decades. In my E. L. Thorndike address I summarized the temporal trends in sex differences for 8 fields and considered factors that drive both the underrepresentation of women and its recent narrowing. I reviewed evidence concerning sex differences in mathematical and spatial aptitude, biases in hiring, funding, publishing, remuneration, and promotion, and gendered preferences. I conclude that the most important causes of underrepresentation appear to occur before women matriculate in college and are concerned with ability-related beliefs, stereotypes, and preferences starting in early elementary school, which by the end of high school have reduced the size of the potential pool. By the time women reach graduate school, there is evidence that they are as successful as their male counterparts in being interviewed and hired for tenure-track positions, funded, and published.

      Of course, an alternative explanation would be that the female candidates tend to be better than the males, perhaps because those few females who manage to survive and thrive in male-dominated fields need to be especially gifted. Ceci and Williams assessed this hypothesis in several ways. First, they compared male and female applicants on various objective measures of productivity, including number of publications, citation counts and grant capture. The comparison revealed no overall difference in the quality of male vs. female applicants (Ceci et al., 2014).

      Die Studie:

      Much has been written in the past two decades about women in academic science careers, but this literature is contradictory. Many analyses have revealed a level playing field, with men and women faring equally, whereas other analyses have suggested numerous areas in which the playing field is not level. The only widely-agreed-upon conclusion is that women are underrepresented in college majors, graduate school programs, and the professoriate in those fields that are the most mathematically intensive, such as geoscience, engineering, economics, mathematics/computer science, and the physical sciences. In other scientific fields (psychology, life science, social science), women are found in much higher percentages.

      In this monograph, we undertake extensive life-course analyses comparing the trajectories of women and men in math-intensive fields with those of their counterparts in non-math-intensive fields in which women are close to parity with or even exceed the number of men. We begin by examining early-childhood differences in spatial processing and follow this through quantitative performance in middle childhood and adolescence, including high school coursework. We then focus on the transition of the sexes from high school to college major, then to graduate school, and, finally, to careers in academic science.

      The results of our myriad analyses reveal that early sex differences in spatial and mathematical reasoning need not stem from biological bases, that the gap between average female and male math ability is narrowing (suggesting strong environmental influences), and that sex differences in math ability at the right tail show variation over time and across nationalities, ethnicities, and other factors, indicating that the ratio of males to females at the right tail can and does change. We find that gender differences in attitudes toward and expectations about math careers and ability (controlling for actual ability) are evident by kindergarten and increase thereafter, leading to lower female propensities to major in math-intensive subjects in college but higher female propensities to major in non-math-intensive sciences, with overall science, technology, engineering, and mathematics (STEM) majors at 50% female for more than a decade. Post-college, although men with majors in math-intensive subjects have historically chosen and completed PhDs in these fields more often than women, the gap has recently narrowed by two thirds; among non-math-intensive STEM majors, women are more likely than men to go into health and other people-related occupations instead of pursuing PhDs.

      Importantly, of those who obtain doctorates in math-intensive fields, men and women entering the professoriate have equivalent access to tenure-track academic jobs in science, and they persist and are remunerated at comparable rates—with some caveats that we discuss. The transition from graduate programs to assistant professorships shows more pipeline leakage in the fields in which women are already very prevalent (psychology, life science, social science) than in the math-intensive fields in which they are underrepresented but in which the number of females holding assistant professorships is at least commensurate with (if not greater than) that of males. That is, invitations to interview for tenure-track positions in math-intensive fields—as well as actual employment offers—reveal that female PhD applicants fare at least as well as their male counterparts in math-intensive fields.

      Along these same lines, our analyses reveal that manuscript reviewing and grant funding are gender neutral: Male and female authors and principal investigators are equally likely to have their manuscripts accepted by journal editors and their grants funded, with only very occasional exceptions. There are no compelling sex differences in hours worked or average citations per publication, but there is an overall male advantage in productivity. We attempt to reconcile these results amid the disparate claims made regarding their causes, examining sex differences in citations, hours worked, and interests.

      We conclude by suggesting that although in the past, gender discrimination was an important cause of women’s underrepresentation in scientific academic careers, this claim has continued to be invoked after it has ceased being a valid cause of women’s underrepresentation in math-intensive fields. Consequently, current barriers to women’s full participation in mathematically intensive academic science fields are rooted in pre-college factors and the subsequent likelihood of majoring in these fields, and future research should focus on these barriers rather than misdirecting attention toward historical barriers that no longer account for women’s underrepresentation in academic science.

      Second, they conducted a large-scale hiring-decision study: the largest such study to date (Williams & Ceci, 2015). The pair sent hypothetical job applications from identically qualified applicants to tenure-track professors in biology, economics, engineering and psychology, and asked them to assess the applicants’ suitability for a tenure-track position. The final sample included nearly 900 professors from 371 US universities. Averaging across conditions, Williams and Ceci found a 2:1 bias in favour of female applicants. This pro-female bias was found in all four fields and among both male and female faculty. (The only exception was male economics professors, who showed no significant bias in either direction.) Thus, rather than being biased against women, this study suggests that, when it comes to employment decisions, STEM faculty are biased in their favour.8

      Die Studie hatte ich bereits hier besprochen

      As well as finding little evidence for anti-female bias in hiring, Ceci and Williams find little evidence for bias in college admission, recommendation letters, promotions, article acceptances, citations or grant funding (Ceci & Williams, 2011Ceci et al., 20142020). Studies that purport to find such bias are often widely discussed and cited (e.g. Budden et al., 2008, on gender bias in acceptance rates for papers first-authored by females,9 and Wennerås & Wold, 1997, on gender bias in grant success). However, according to Ceci and Williams (2011), a systematic review of all the available evidence suggests that deviations from gender equality are rare, and that they just as often favour women as men. Again, this is not what we would expect if anti-female bias were endemic.

      In der Tat passt das nicht ganz zu der Opfertheorie. Aber das will man in den Gender Studies natürlich nicht wahrnehmen.

      Earlier, we listed some of the studies finding anti-female bias in STEM and academia in general. In the interests of balance, we now present a comparable list of some of the studies that failed to find such bias, or that arguably found bias in the opposite direction.

      • Comparisons of gender-blind and non-blind assessments suggest that teachers sometimes favour girls when evaluating student achievement. For example, one study found that French middle-school teachers favour girls in maths assessments (Terrier, 2020), while another found that Israeli high school teachers favour girls in assessments in both the sciences and the humanities (Lavy, 2008).

      • At some elite universities, the academic threshold for admission is higher for men than for women. This is true, for instance, at Oxford University in the UK (Bhattacharya et al., 2017) and Harvard University in the US (Arcidiacono et al., 2019, Table D5).

      • STEM professors are more receptive to meeting requests from female students than male students (C. Young et al., 2019).
      • Female college students in male-dominated fields are less likely than other female students to switch majors: the opposite of what one would expect if women faced an especially hostile environment in these fields. Male students in female-dominated fields, on the other hand, are more likely to switch majors (Riegle-Crumb et al., 2016).
      • The STEM pipeline from bachelor’s degree to PhD no longer leaks more women than men (Miller & Wai, 2015; see also Porter & Ivie, 2019).
      • In teacher accreditation exams in France, examiners discriminate in favour of women in male-dominated fields (and, to a lesser extent, in favour of men in female-dominated ones; Breda & Hillion, 2016).

      • Although fake-résumé audit studies sometimes find anti-female bias, often they find no bias or bias in favour of women (Baert, 2018). The findings with respect to gender are much more mixed than those for race/ethnicity.

      • Higher-ranked computer science departments recruit women at above-expected rates, relative to the number of female computer scientists (and, as a result, lower-ranked institutions end up recruiting women at below-expected rates; Way et al., 2016).
      • In one large study (N = 1599), South African students watching lectures with identical slides and scripts, but with the sex of the lecturer varied, gave higher ratings to female lecturers than to male (Chisadza et al., 2019).
      • Female scientists attribute higher levels of science-related traits such as objectivity, rationality and intelligence to their female colleagues than their male colleagues; male scientists, in contrast, attribute similar levels of these traits to colleagues of both sexes (Veldkamp et al., 2017).
      • In one large-scale experiment (N = 989), reviewers in the biosciences rated articles just as favourably if told that the author was a woman as they did if told the author was a man (Borsuk et al., 2009).
      • An analysis of journal articles from 145 journals and 1.7 million authors found no evidence for bias against female authors in the peer-review process (Squazzoni et al., 2020).
      • Although some studies find higher journal-article acceptance rates for men, studies that control for factors such as publication record and academic rank have generally found either no sex differences (e.g. Blank, 1991Card et al., 2020) or higher acceptance rates for women (e.g. Lerback & Hanson, 2017).
      • In computer science, conference papers that include female authors are just as likely to be accepted when the reviewers know the authors’ names (and thus potentially their sex) as when they don’t have this information (Tomkins et al., 2017).
      • An analysis of 10,000 papers in social-science journals found that female-led papers are just as likely to be cited as male-led papers (Lynn et al., 2019).
      • A large meta-analysis found no evidence that men were more likely than women to be awarded grants, and some evidence for the reverse. The absence of a male advantage was robust across academic fields, nations and year of awards (Marsh et al., 2009).
      • One study found that, without controlling for research productivity and NIH experience, men and women were just as likely to receive NIH grants; however, when controlling for these variables, women were more likely to receive them (Ginther et al., 2016).
      • In a large US experiment, NIH-grant proposals were rated just as favourably when the supposed principal investigator (PI) was a woman as they were when the PI was a man (Forscher et al., 2019).
      • In Sweden, medical grant proposals headed by women are given scores 10% higher than those headed by men, all else being equal (Sandström & Hällsten, 2008).
      • An analysis of the publication records of 1345 recently promoted Swedish professors found no evidence that women are held to a higher standard than men when it comes to promotion. In fact, in some fields, men may be held to a higher standard (Madison & Fahlman, 2020).
      • An analysis of archival promotion data found that women in IT were more likely to be promoted than men, contrary to the researchers’ predictions (Langer et al., 2020).
      • Among German sociologists, women can get tenure with 23–44% fewer publications than men (Lutter & Schröder, 2016).


      As with studies finding anti-female bias, it would no doubt be possible to pick holes in individual studies. Again, though, the sheer number of studies finding no gender disparities, or finding a female advantage, suggests that we should take the general thrust of the evidence seriously – just as we should with the studies showing anti-female bias.

      Eine schöne Aufstellung. Ich werde in Diskussionen denke ich noch häufiger darauf zurückkommen.

      A mixed picture

      In summary, it seems fair to say that the evidence for gender discrimination in STEM is mixed, with some studies finding pro-male bias, some finding the reverse and some finding none at all. What should we conclude? In our view, there are two main interpretations. The first is that the apparently mixed findings are not in fact inconsistent. Rather than there being uniform bias against women, or uniform bias against men, there are pockets of bias against both sexes (and presumably no gender bias at some institutions and in some cases). The second interpretation is that, at this stage, the findings are inconclusive: the jury is still out. But this in itself suggests that sex-based discrimination could not be hugely prevalent in STEM; if it were, it would be easier to detect a clear signal and the research would paint a more consistent picture of the situation. This, in turn, suggests that factors other than discrimination – in particular, sex differences in occupational preferences – are the main explanation for the persistence of gender gaps in STEM.10

      In der Tat wäre ansonsten ein wesentlich klareres Bild zu erwarten. Aber das lässt sich natürlich alles beiseite wischen, wenn man einfach darauf abstellt, dass das Patriarchat die waren Umstände verschleiert oder etwas in dieser Art.

      A hidden barrier to the progress of women in STEM?

      Before shifting topics, we should briefly consider another potential barrier to the progress of women in STEM – one that is often overlooked: stereotypes of the sexist academy. In the quest to promote women in STEM, academics and activists may sometimes inadvertently overstate the ubiquity of bias and discrimination against women in this sector. An unintended consequence may be to scare away some women who would otherwise be interested in a STEM career (Sesardic & De Clercq, 2014Williams & Ceci, 2015). Diekman et al. (2017) point out that people’s decision to enter or avoid a field is shaped to an important degree by their beliefs about the culture of the field in question (see also Cheryan et al., 2015). If women are given the impression that the STEM workplace is a hotbed of sexism and an unwelcome place for women, many might quite understandably decide to look for other fields in which to make their mark (Adams et al., 2006Ganley et al., 2018Thoman & Sansone, 2016). Ironically, the consequent dearth of women in STEM might then itself be taken as further evidence that STEM is a hotbed of sexism, creating a self-reinforcing, vicious cycle.

      Das ist auch ein Argument, dass Feministen nicht mögen: Sie führen zum einen an, dass kleinste Anzeichen für Ungleichbehandlung Frauen abschrecken, sind aber diejenigen, die solche Anzeichen am nachhaltigsten behaupten. In der feministischen Darstellung sind diese Bereiche die schlichte Hölle für Frauen. Weil das ihrer Agenda gut tut. Aber das sie selbst Frauen abschrecken könnten und das es nach ihren eigenen Theorien dann besser wäre Frauen anzulügen und die Diskriminierung zu leugnen wird eher nicht positiv aufgenommen.

      Needless to say, if the STEM workplace really were a hotbed of sexism, this would be something we would need to confront, even if doing so put off some budding female scientists. However, given that the evidence for pervasive sexism in STEM is mixed, and that at least some experts conclude that – for the most part – STEM is fair for women and discrimination rare, conveying such a dark image of the STEM workplace may do more harm than good. See Figure 3 for a summary of the many factors contributing to the gender gaps in STEM.


      Figure 3. Occupational outcomes are a product of many different factors; workplace discrimination is only one among many.

      Fand ich wiederum einen sehr erhellenden Teil der Studie mit vielen guten Studien.