Gründe für Geschlechterunterschiede in STEM: Geschlechtsunterschiede in der Variabilität (Teil 3)

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:

Geschlechtsunterschiede in der Variabilität

Los geht es:

We see, then, that small mean differences in certain STEM-relevant aptitudes may result in somewhat more males than females occupying the right-hand tail of the distribution for those aptitudes. However, even if there were no differences at the mean, males could still outnumber females among the minority at the right-hand tail. This is because males and females differ in another way as well. In a wide variety of traits, males as a group are more variable than females: the male distribution is slightly flatter, and stretches out somewhat further on both sides of the mean (see Figure 2).

Das hatten wir hier auch schon in verschiedenen Diskussionen, etwa zum Thema Intelligenz

This is the case for a range of physical traits, including birth weight, adult weight, adult height and running speed (Lehre et al., 2009), average heart rate during exercise (Hossack & Bruce, 1982) and various aspects of brain structure (Ritchie et al., 2018Wierenga et al., 2020). It also appears to be the case for a range of psychological traits, including creativity (Karwowski et al., 2016), general knowledge (Feingold, 1992), physical aggression (Archer & Mehdikhani, 2003) and at least four of the Big Five personality traits (Borkenau et al., 2013). Of particular relevance to the present topic, males seem to be more variable than females in a number of cognitive abilities relevant to STEM (Baye & Monseur, 2016Feingold, 1992). In this section, we outline these differences, then make the case that they are shaped to a significant degree by biological factors.

Aus der Studie von Baye und Monseur:

This study examines gender differences in the variability of student performance in reading, mathematics and science. Twelve databases from IEA and PISA were used to analyze gender differences within an international perspective from 1995 to 2015. Effect sizes and variance ratios were computed. The main results are as follows. (1) Gender differences vary by content area, students‘ educational levels, and students’ proficiency levels. The gender differences at the extreme tails of the distribution are often more substantial than the gender differences at the mean. (2) Exploring the extreme tails of the distributions shows that the situation of the weakest males in reading is a real matter of concern. In mathematics and science, males are more frequently among the highest performing students. (3) The “greater male variability hypothesis” is confirmed.

Aus der Studie von Karwowski:

Do men vary more than women in personality? Evolutionary, genetic, and cultural arguments suggest that hypothesis. In this study we tested it using 12,156 college student raters from 51 cultures who described a person they knew well on the 3rd-person version of the Revised NEO Personality Inventory. In most cultures, male targets varied more than female targets, and ratings by female informants varied more than ratings by male informants, which may explain why higher variances for men are not found in self-reports. Variances were higher in more developed, and effects of target sex were stronger in more individualistic societies. It seems that individualistic cultures enable a less restricted expression of personality, resulting in larger variances and particularly so among men.
► We studied effects of sex on the variances in personality descriptions. ► Informant reports of personality varied more for male than for female targets. ► Descriptions by female informants varied more than descriptions by male informants. ► Across 51 cultures, both effects were stronger in more individualistic societies.

Es zeigen sich also weitere Spannen gerade bei Männern, die sich dann bei der Verteilung im Spitzenbereich auswirken:


Figure 2. A fundamental sex difference in humans and many other animals: for a wide range of traits, males are more variable than females. As such, although only a small percentage of people occupy either extreme of the distribution, more males do than females, even when the mean scores for both sexes are identical.

Hier sieht man gut den höheren Anteil durch den „flacheren“ Verlauf: Bereits im hinteren Drittel sieht man, dass die Anzahl der Männer mit höheren Werten deutlich über denen der Frauen liegt. Schaut man ganz aufs Ende zeigt sich ein noch deutlicherer Unterschied bezogen auf eine kleinere Anzahl von Personen. 

Variability in STEM-relevant cognitive capacities

Specific cognitive capacities

To begin with, many studies have found somewhat greater variability among males than females in specific cognitive capacities, including mathematical aptitude, spatial ability and science knowledge. In one classic paper, Hedges and Novell (1995) analysed the cognitive test scores of six large, nationally representative US samples, together covering a 32-year period. They found that, for 35 of the 37 tests examined, male variability was greater than female. Importantly, this included all the tests of mathematics, spatial ability, mechanical reasoning and science knowledge. In most cases, sex differences in average scores were small. Nevertheless, because males were more variable, they tended to outnumber females among the minority with especially high scores. (An exception was reading comprehension, for which males outnumbered females at the bottom – the usual pattern – but females outnumbered males at the top.)

Und gerade die Leute, die in den Bereichen besonders gut sind (und nicht gleichzeitig noch in anderen Bereichen ebenfalls gut sind) werden eben in die STEM-Bereiche gehen, einfach weil sie dort die besten Leistungen in der Vergangenheit gebracht haben und ihnen das liegt. 

Similar results have been found in other nations and using other tests. For example, in a large sample of UK students (N ≈ 320,000), Strand et al. (2006) found that, although sex differences were small at the mean, males somewhat outnumbered females at the top and the bottom of the distribution for both quantitative and nonverbal reasoning; for verbal reasoning, in contrast, males outnumbered females only at the bottom (see Lohman & Lakin, 2009, for a US replication of this exact pattern). Likewise, analyses of data from the Organisation for Economic Co-operation and Development (OECD) and the International Association for the Evaluation of Educational Achievement (IEA) show that, in most countries for which test scores are available, males are more variable than females in maths, reading and science (Baye & Monseur, 2016Machin & Pekkarinen, 2008). More recently, an analysis of 1.6 million students by O’Dea et al. (2018) found again that, although on average girls did better than boys at school, boys exhibited greater variability and thus outnumbered girls among the highest performers. For grades in STEM subjects, the top 10% contained equal numbers of boys and girls; any higher than the top 10%, however, contained more boys. Note that the variability gap was somewhat smaller for grades than for test scores, perhaps as a result of ceiling effects for the former. Curiously, the variability gap was larger for non-STEM subjects than for STEM ones, contrary to the authors’ predictions.

Aus der O’Dea Studie:

Fewer women than men pursue careers in science, technology, engineering and mathematics (STEM), despite girls outperforming boys at school in the relevant subjects. According to the ‘variability hypothesis’, this over-representation of males is driven by gender differences in variance; greater male variability leads to greater numbers of men who exceed the performance threshold. Here, we use recent meta-analytic advances to compare gender differences in academic grades from over 1.6 million students. In line with previous studies we find strong evidence for lower variation among girls than boys, and of higher average grades for girls. However, the gender differences in both mean and variance of grades are smaller in STEM than non-STEM subjects, suggesting that greater variability is insufficient to explain male over-representation in STEM. Simulations of these differences suggest the top 10% of a class contains equal numbers of girls and boys in STEM, but more girls in non-STEM subjects.

Ich hatte deswegen beschlossen, diese Studie nach und nach und in mehreren Beiträgen zu besprechen, weil sie eine so gute Zusammenstellung diverser Quellen ist und die Wissenschaft dazu umfangreich aufschlüsselt.

Ist es nicht traurig, dass diese ganze Problematik in anderen Bereichen, von Gender Studies bis zur Politik schlicht mit „alte weiße Männer und ihr Patriarchat verhindern durch ihr Patriarchat, dass die armen Frauen nicht nach oben kommen“ abgetan wird. Hier wird – wie es in der Wissenschaft üblich sein sollte – ein Problem von einer Vielzahl von Problemen beleuchtet und eine Vielzahl von Faktoren anhand von Studien erläutert. Auf der „anderen Seite“ hätte man wahrscheinlich schlicht einen Hinweis darauf, dass ja mehr Männer in den Positionen sind und Männer allgemein als Gruppe nun einmal die Macht haben. 

General cognitive ability

As well as greater male variability in specific cognitive aptitudes, males may be more variable in general cognitive ability or IQ (Deary et al., 2007Feingold, 1992Strand et al., 2006). The gold-standard study on this topic is Johnson et al. (2008). Unlike earlier studies, which used potentially unrepresentative samples, Johnson and colleagues utilized IQ data from two population-wide surveys of 11-year-old school children in Scotland. As expected, IQ variability was greater among boys than girls, such that there were somewhat more boys at both extremes of the IQ distribution: more at the top, but also more at the bottom (although see Iliescu et al., 2016, for a recent failure to replicate this pattern in a large, nationally representative Romanian sample).

Der Artikel von Johnson:

The idea that general intelligence may be more variable in males than in females has a long history. In recent years it has been presented as a reason that there is little, if any, mean sex difference in general intelligence, yet males tend to be overrepresented at both the top and bottom ends of its overall, presumably normal, distribution. Clear analysis of the actual distribution of general intelligence based on large and appropriately population-representative samples is rare, however. Using two population-wide surveys of general intelligence in 11-year-olds in Scotland, we showed that there were substantial departures from normality in the distribution, with less variability in the higher range than in the lower. Despite mean IQ-scale scores of 100, modal scores were about 105. Even above modal level, males showed more variability than females. This is consistent with a model of the population distribution of general intelligence as a mixture of two essentially normal distributions, one reflecting normal variation in general intelligence and one refecting normal variation in effects of genetic and environmental conditions involving mental retardation. Though present at the high end of the distribution, sex differences in variability did not appear to account for sex differences in high-level achievement.

Lawrence Summer, der damalige Präsident der Harvard Universität, musste nach einer Rede in 2005, bei der er darauf abstellte, dass die geringere Beteiligung von Frauen in STEMFächern auch mit dieser Verteilung zusammenhing, zurücktreten. 

Es scheint aber gute Gründe für diese Annahme zu geben. 

To the extent that greater male variability results in more males than females occupying the upper echelons of ability, whether for specific aptitudes or general cognitive ability, this may help to explain why more males than females occupy the upper echelons of certain fields in STEM (Levy & Kimura, 2009Steven Pinker, 2002). It is important to emphasize that this could not be a complete explanation of observed STEM gender gaps. As various experts have pointed out, sex differences in variability are not nearly large enough to explain these gaps in their entirety (Hyde, 2014Johnson et al., 2008O’Dea et al., 2018). Moreover, variability differences would not explain gender gaps at lower levels of the STEM hierarchy (where extreme abilities are not required), and would not explain why the gaps are larger in some fields than others. Still, taken together with preferences, cognitive specializations and stereotypes, greater male variability may be one more piece of the STEM puzzle.

Ich vermute mal den Autoren war bekannt, dass das ein sehr vermientes Gebiet ist, so dass sie diese Erläuterungen, die aber auch so sinnvoll sind, noch einmal nachgeschoben haben. 


See Box 2 for further discussion.

Box 2 Exploring the implications of greater male variability.

Ich füge es auch noch mal als Text ein:

Box 2 Exploring the implications of greater male

The claim that men are more variable than women in cognitive ability is controversial. Here are some questions to ask about this claim:

1. Is it sexist? Is it sexist even if it turns out to be true?

Aus meiner Sicht nein. Aber die Frage, ob etwas wahres falsch sein kann würde in den Gender Studies sicherlich damit beantwortet werden, dass es in einer von Männern geprägten Welt keine Wahrheiten geben kann. 

2. If it is sexist against women to say that there are more men than women at the highest levels of ability, is it sexist against men to say that there are also more men than women at the lowest levels? If not, how might we explain this asymmetry?

Fakten sind an sich nie sexistisch. Herleitungen daraus können es durchaus sein. Und in der Tat ist es dann eine interessante Frage, ob man nur nach oben schauen darf oder dann auch nach unten schauen müsste und – böses Wort – eine Diskriminierung von Männern im unteren Bereich annehmen müsste.
Im Feminismus würde man wahrscheinlich antworten: Natürlich ist es sexistisch gegenüber Frauen, weil es Frauen davon abhält und abschreckt im STEM tätig zu werden und Stereotype verfestigt. Es ist nicht sexistisch gegen Männer, weil es nur eine Folge des Patriarchats ist, das eben auch Männern schadet. Mit Feminismus würde alles besser werden.

3. Assume for a moment that males really are more variable in cognitive ability. Should we suppress this information? Could we suppress it, even if we wanted to?

Nein, wenn es so wäre, dann sollte man es auch sagen dürfen. Es kann ja genauso ein guter Hinweis sein: Seht her, ihr werdet gar nicht diskriminiert, wenn ihr gut in dem Bereich sind, dann stehen euch alle Türen offen. 

Aus feministischer Sicht sollte man es natürlich unterdrücken und könnte das auch. Das geht ganz einfach.

4. Might it be possible instead to emphasize the importance of avoiding exaggerating small differences, of keeping sight of the variation among individuals within each sex, and of treating individuals as individuals, rather than as instantiations of the statistical properties of the groups to which they belong?

Leute als Individuum zu sehen verschleiert natürlich nur die Unterdrückung und kann daher auch nur von einem (weißen) Mann kommen, der solche Unterdrückung nicht kennt!!1

The nature and nurture of sex differences in variability

What might explain sex differences in cognitive variability? Given that the magnitude of these differences fluctuates across cultures and times, it seems unlikely that they are attributable solely to biological factors (Feingold, 1992Gray et al., 2019Hyde et al., 2009). However, as with average differences in preferences and aptitudes, various lines of evidence suggest that biological factors play a crucial role.

First, greater male variability is found not only in psychological traits, but also in traits that are largely impervious to social pressure and cultural norms, such as height, birth weight and BMI (Lehre et al., 2009). The sex differences in psychological variability thus appear to be part of a broader pattern. Considerations of both parsimony and plausibility suggest that this pattern probably has a single, common cause, rather than distinct causes for its physical and psychological components.

Das ist eine schöne Voranstellung des Grundprinzips. In der Tat liegt ja die Vermutung nahe, dass, wenn man es in einer Vielzahl anderer, recht eindeutig nicht sozial begründeter Faktoren vorfindet, vieles dafür spricht, dass auch dieses Element lediglich auf dem auch bei den anderen Faktoren vorliegenden Gründen beruht. Es ist als letztendlicher Beleg nicht er stärkste, weil es kein unmittelbarer Nachweis ist, aber es ordnet die ganze Theorie in einen größeren Zusammenhang ein. 

Second, sex differences in variability emerge in early childhood (O’Dea et al., 2018). The sex difference in IQ variability, for instance, appears before children begin school (Arden & Plomin, 2006). This does not definitively rule out a Nurture Only explanation for the difference. However, it does add some weight to the scales on the biological side of the argument, and it reduces the range of non-biological factors that any Nurture Only explanation can invoke.

In der Tat macht es das schwieriger, weil dort viele Unterschiede eben noch gar nicht so wichtig sind. Aber das Gegenargument wird sein, dass die Kinder auch in jungen Jahren in einer von Männern geprägten Gesellschaft aufwachsen und das eben schon ganz früh aufnehmen. 

Das in dem Bereich gerade Frauen die Kinder betreuen würde dann damit abgewiegelt werden, dass die auch in einer von Männern geprägten Welt leben etc. 

Whatever the ultimate causes of greater male variability in IQ, those causes appear to be in place by three years of age at the latest. And other variability sex differences have been detected even earlier. One mega-analysis of brain-imaging data from 16,683 individuals revealed that greater male variability in brain structure was present by one year of age (suggesting a role for genetic factors), and was highly stable throughout the lifespan (suggesting a relatively modest role for the environment; Wierenga et al., 2020).

Die Wierenga Studie:

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta‐Analysis) Consortium presents the largest‐ever mega‐analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1‐90 years old (47% females). We observed significant patterns of greater male than female between‐subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene‐environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex‐specific vulnerability to disorders.

Ein Jahr alt. Verdammt, das Patriarchat ist schnell. Da können sie noch nicht mal sprechen oder viel verstehen, aber das Patriarchat formt ihr Gehirn dennoch bereits um. 

Third, greater male variability is not unique to humans but is found as well in many nonhuman animals, including most mammals (Reinhold & Engqvist, 2013). Among red deer, for instance, males are not only larger than females but are also more variable in size (Clutton-Brock et al., 1982); among primates, males are more variable in lifespan (Colchero et al., 2016); among guenons (a genus of Old World monkeys), males are more variable in skull size (Cardini & Elton, 2017); and among chimpanzees – as among humans – males are more variable in brain structure (DeCasien et al., 2020). When we find this pattern in other mammals, the only realistic explanation is a biological one. When we then find the same pattern in our own species, considerations of parsimony and plausibility suggest again that a biological explanation is appropriate for us, too. Indeed, without a strong reason to think otherwise, the default assumption should be that humans fit within the same explanatory framework that applies to the rest of the animal kingdom, and thus that greater male variability in our species has the same root cause as that in our nonhuman kin.

Die Studie von DeCasien finde ich interessant:

Across the animal kingdom, males tend to exhibit more behavioural and morphological variability than females, consistent with the ‘greater male variability hypothesis‘. This may reflect multiple mechanisms operating at different levels, including selective mechanisms that produce and maintain variation, extended male development, and X chromosome effects. Interestingly, human neuroanatomy shows greater male variability, but this pattern has not been demonstrated in any other species. To address this issue, we investigated sex-specific neuroanatomical variability in chimpanzees by examining relative and absolute surface areas of 23 cortical sulci across 226 individuals (135F/91M), using permutation tests of the male-to-female variance ratio of residuals from MCMC generalized linear mixed models controlling for relatedness. We used these models to estimate sulcal size heritability, simulations to assess the significance of heritability, and Pearson correlations to examine inter-sulcal correlations. Our results show that:
(i) male brain structure is relatively more variable;
(ii) sulcal surface areas are heritable and therefore potentially subject to selection;
(iii) males exhibit lower heritability values, possibly reflecting longer development; and
(iv) males exhibit stronger inter-sulcal correlations, providing indirect support for sex chromosome effects.
These results provide evidence that greater male neuroanatomical variability extends beyond humans, and suggest both evolutionary and developmental explanations for this phenomenon.

Gut, Schimpansen haben ja häufig auch ein Patriarchat, insofern belegt es allenfalls spezienübergreifende Folgen der Diskriminierung. 

Evolutionary rationale

If greater male variability has a biological basis, what evolutionary pathways might have produced it? Once again, the answer is not yet certain, but biologists have put forward a number of plausible suggestions. Two in particular stand out: one adaptationist explanation and one non-adaptationist explanation.

The adaptationist explanation traces greater male variability in general to another, more fundamental sex difference: greater male variability in reproductive success (see, e.g. Pomiankowski & Møller, 1995Rowe & Houle, 1996). As a result of sex differences in parental investment, males in many species are more variable than females in the number of offspring they produce (Clutton-Brock & Vincent, 1991Trivers, 1972). At one extreme, some males have a relatively high number of offspring: more than any female. At the other, because mating opportunities are finite, some males have no offspring or relatively few. Most females, in contrast, fall somewhere in between. In species where male reproductive variability is high, selection favours any trait that increases a male’s chances of being among the few that have many offspring, rather than the many that have few or none. One such trait appears to be risk-proneness. In many species, selection has favoured a greater willingness among males to risk life and limb in the pursuit of status, resources and mating opportunities. Male risk-taking sometimes paid off for the risk-taker and sometimes did not. When it did pay off, however, it paid off so handsomely that, on average, risk-taking males had more offspring than males who were more risk-averse. For females, in contrast, risk-taking offered fewer reproductive advantages, because the ceiling number of offspring for females is so much lower. Thus, males in many species evolved a greater propensity to take risks than did females (Daly & Wilson, 2001).

According to the reproductive-variability explanation for greater male variability, this calculus applies not only to behaviour but to development: male development is somewhat more ‘risk-prone’ than female development, such that males have a greater chance of developing especially impressive traits but also a greater chance of developing less impressive ones. The former males have a sufficiently high number of offspring that, on average, males with the risky developmental programme have more offspring than those with a more conservative or risk-averse one. As such, the risk-prone male developmental programme is selected – and with it, greater male variability in a wide range of traits.

Might this apply to humans? Compared to most mammals, the human sex difference in reproductive variability is rather modest (Stewart-Williams & Thomas, 2013b). Nonetheless, genetic and anthropological data strongly suggest that there is such a difference (Betzig, 2012Labuda et al., 2010; summarized in M. L. Wilson et al., 2017, Table 1). This may have resulted in the evolution of males that are somewhat more risk-prone than their female counterparts, not just behaviourally but developmentally as well.

An alternative, non-adaptationist explanation is that sex differences in variability are a byproduct of the fact that, in our species and many others, biological sex is determined by sex chromosomes (as opposed, for instance, to temperature; Johnson et al., 2009Reinhold & Engqvist, 2013). In many species with chromosomal sex determination, one sex is heterogametic (members of that sex have two different sex chromosomes), whereas the other is homogametic (members have two identical sex chromosomes). In mammals, males are heterogametic (XY chromosome), whereas females are homogametic (XX); in birds, it’s the other way round (ZW females vs. ZZ males). According to the ‘sex-chromosome hypothesis,’ in species with this arrangement, the heterogametic sex is usually more variable. This is because the sex chromosome unique to the heterogametic sex (e.g. the Y chromosome in mammals) typically has very few genes, other than those that trigger the development of that sex. As a result, the heterogametic sex has only one copy of most genes on the non-unique sex chromosome (e.g. the X chromosome). In contrast, the homogametic sex has two copies. If these copies differ from one another, their effects on their owner are typically averaged, which reins in the effect of any extreme genes. For the heterogametic sex, on the other hand, with just one copy of most genes, there is rarely any reining in of extreme genes. The net effect is that, for any trait influenced by the sex chromosome shared by both sexes, the heterogametic sex is normally more variable.

Consistent with this hypothesis, Reinhold and Engqvist (2013) found that, in two groups of species with heterogametic males (mammals and certain insects), the males tended to be more variable in body size, whereas in two groups with heterogametic females (birds and butterflies), the females tended to be more variable. Note that, as well as providing initial support for the sex-chromosome hypothesis, these findings cast doubt on the reproductive-variability explanation, which would predict greater male variability across the board (although see Wyman & Rowe, 2014). For other evolutionary explanations of greater male variability, see Archer and Mehdikhani (2003) and Del Giudice et al. (2018).

Certainly, as Hyde et al. (2009) have shown, the size of the variability gender gap varies across cultures, suggesting that social forces play a role in shaping the gap – perhaps enlarging it and perhaps sometimes making it smaller. The basic pattern itself, however, is plausibly a part of our evolutionary heritage: one that helps to shape the modern occupational landscape.

Das sind alles mögliche Erklärungen.

13 Gedanken zu “Gründe für Geschlechterunterschiede in STEM: Geschlechtsunterschiede in der Variabilität (Teil 3)

  1. Christian, Texte wie dieser hier von dir führen dazu das Menschen sich übel fühlen.

    Die haben jetzt ein paar Jahre die unbelegte Behauptung aufgestellt oder gehört und geglaubt, das es keine Unterschiede zwischen Männer und Frauen gibt und das diese Unterschiede auf Frauendiskriminierung beruhen. In ihrer Welt ist es besser wenn Frau diskriminiert sind, als wenn es einfach nur Unterschiede zwischen den Geschlechtern gibt, weil das würde ja bedeuten das Männer vielleicht etwas besser können.

    Wie man an dem Tweet vom VaginaMuseum sieht, ist selbst ein Ergebnis das die Unterschiede nur „partly“ erklärt „Pseudoscience“

    Die Frau Dr. hier bezieht das Ergebnis auch sich selbst und kann nicht verstehen wie jemand sowas sagen kann.

    „I thought we were past this.“

    Die feministische Propaganda hat doch schon die neue Wahrheit verkündet, das die Gesellschaft Frauen hasst und STEM ein Hort des übelsten frauenfeindlichsten Sexismus ist (Frauen, bitte jetzt Bewerben). Wieso zweifeln die Leute noch daran??!

    • Es ist so aussichtslos mit diesen Frauen. Klar gibt es Professoren und auch Lehrer die sexistisch sind. Aber a) kann man das bei Professoren nicht wissen bevor man überhaupt angefangen hat zu studieren und b) erinnere ich mich noch daran wie die Mädchen das einem Lehrer immer vorgeworfen haben, der aber meiner Meinung nach eigentlich nur einen ätzenden Sarkasmus pflegte, der eigentlich jedem zuteil wurde, aber pubertierende Mädels darauf hysterisch reagierten. Wundert mich auch zunehmend weniger wenn man sich vor Augen führt, dass die gerade alles haben können, weil sie attraktiv sind und Männer drauf stehen. Da ist ein Physiklehrer der ihre Grenzen sarkastisch kommentiert eher nicht was sie möchten.

        • Ich hab grad den Quillette Artikel gefunden.

          As far as we can determine, there are four main theses running through the book:

          • ‘Race’ is not a meaningful biological category
          • Genes can only contribute to population differences on certain “superficial” traits
          • Studying whether genes might contribute to population differences on non-superficial traits is tantamount to “scientific racism”
          • Almost everyone interested in whether genes might contribute to population differences on these other traits is a “scientific racist”
          To be blunt, we disagree with all four of Saini’s main theses, as we shall explain in this article.

          Saini ist eine SJW-Inspirierte Journalistin, die nicht gut in Bio ist, aber oben als Beleg dafür angeführt wird, dass echte Biologen nur „Pseudoscience“ betreiben.

          • Die Diskussion erinnert mich an einen Satz des linken Vordenkers Noam Chomsky. Aus der Erinnerung: „Kann schon sein, dass Schwarze einen geringeren IQ haben als Weiße, aber ich will es nicht wissen.“
            Ich glaube, er hatte nicht „im Mittel“ gesagt, aber das war offensichtlich gemeint.

            Hat mir gefallen, weil er sich geweigert hatte, wissenschaftliche Erkenntnisse zu leugnen.

          • „Kann schon sein, dass Schwarze einen geringeren IQ haben als Weiße, aber ich will es nicht wissen.“

            Ich glaube das gibt es ähnliche Effekte wie bei Studien, die Ergebnis haben das Frauen in irgendwas schlechter als Männer sind oder selbst schuld sind.

            Wenn irgendwer behaupten würde das Schwarze längere Penisse haben, dann würde die gleiche Person das wahrscheinlich als Naturgegeben hinnehmen 🙂

            Wie AiU es mal ausgedrückt hat:

            IQ = a * 1/Penislänge

            Mit Schwarzen an einem Ende und Ostasiaten am anderen Ende. So hat jeder seine Vor-und Nachteile.

  2. Pingback: „Zur Unterrepräsentation von Frauen in MINT“ | ☨auschfrei

  3. Pingback: Die ideologische Weigerung, evolutionär entstandene Geschlechtsunterschiede anzuerkennen | Alles Evolution

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