Gibt es ein Beispiel, wo die feministische Theorie die kausalen Faktoren richtig benennt?

Gute Frage eigentlich. Gibt es irgendein Beispiel, wo die feministische Theorie mit ihren Erklärungen richtig liegt?




Warum brillante Frauen eher Karrieren außerhalb der STEM-Fächer wählen

Ein interessanter Artikel führt Gründe an, die eher dazu führen, dass Frauen sich nicht für Berufe in den „STEM-Fächern“ (Naturwissenschaften, Technik, Ingenieurwissenschaften und Mathematik) entscheiden.

Der erste liegt in „Sachen vs Personen“

Things versus people.Su et al (2009) performed a meta-analysis of studies including a total of over 500,000 people examining gender differences in interests.  Despite claims that gender differences are typically “small” (Hyde, 2005), Su et al found a gigantic gender difference in interests.  Women preferred working with people, whereas men preferred working with things, a preference that is detectable within the first two days of birth and among our close species relatives, rhesus monkeys!  To be sure, these differences were not absolute.  Not every man prefers working with things, and not every woman prefers working with people.  But the effect size was d= .93, and even if you are not familiar with effect sizes, this would make it one of the largest effects in social psychology; it is gigantic.

Dieser grundlegende Unterschied war hier auch schon häufiger angesprochen worden. Der Effekt scheint mir sehr robust immer wieder bestätigt zu werden und erklärt, warum Frauen ehemals rein männliche Fächer wie Medizin oder Jura recht problemlos eroberten, während die STEM-Fächer insgesamt einen geringeren Anteil an Frauen haben.

JUST math skills versus math and verbal skills.  This same issue of differing interests was approached in a different way by Wang, Eccles, and Kenny (2013). Disclosure: Eccles was my dissertation advisor and longterm collaborator; I am pretty sure she identifies as a feminist, has long been committed to combating barriers to women, and is one of the most objective, balanced social scientists I have ever had the pleasure to know.

In a national study of over 1,000 high school students, they found that:

1. 70 percent more girls than boys had strong math and verbal skills;

2. Boys were more than twice as likely as girls to have strong math skills but not strong verbal skills;

3. People (regardless of whether they were male or female) who had only strong math skills as students were more likely to be working in STEM fields at age 33 than were other students;

4. People (regardless of whether they were male or female) with strong math and verbal skills as students were less likely to be working in STEM fields at age 33 than were those with only strong math skills.

Here are their conclusions, in their own words (p. 5):

“Results revealed that mathematically capable individuals who also had high verbal skills were less likely to pursue STEM careers than were individuals who had high math skills but moderate verbal skills. One notable finding was that the group with high math and high verbal ability included more females than males…

Our study provides evidence that it is not lack of ability that causes females to pursue non-STEM careers, but rather the greater likelihood that females with high math ability also have high verbal ability and thus can consider a wider range of occupations than their male peers with high math ability, who are more likely to have moderate verbal ability.”

Ich vermute, dass Leute mit guten sprachlichen Eigenschaften auch lieber mit Leuten zusammenarbeiten und Leute mit entsprechenden schlechten Eigenschaften auch eher darauf verzichten. Interessant ist, dass sich dies bei Männern ebenso zeigt. Auch die, die dort beide Fähigkeiten haben wählen eher andere Fächer. Das macht eine reine Beeinflussung durch Geschlechterrollen unwahrscheinlicher.

Es wäre demnach eine Unterscheidung zwischen verschiedenen Stärken vs eine Entscheidung in einem Bereich zu arbeiten, in dem man besonders gut ist. Es passt im übrigen auch zu dem Klischee des Nerds und Geeks, dessen Kompetenzen eher nicht im sozialen Bereich liegen.

Ein weiterer Faktor wären die reinen Zahlen:

The Numbers

The Council of Graduate Schools puts out regular reports, such as this one, that include the gender distribution in various fields.

Council of Graduate Schools
Source: Council of Graduate Schools

Lo and behold, there is not “pervasive evidence of” a gender gap in graduate enrollments, though there is a gap in some STEM fields. Completely consistent with the work by Su et al and by Wang et al, in nearly all fields that are about people, not only is there no gap disadvantaging women, there are actually more women than men! (Health, education, social and behavioral sciences, public administration, arts and humanities, and even biological sciences).  The same report found that, overall, across all fields, the „gap“ is in the „wrong“ direction: 57 percent of enrollees in graduate programs are women.

Even if there is discrimination against women in these fields, it is not preventing women from entering those fields in droves. (Indeed, the logic of “gap = discrimination”—a logic I have repeatedly rejected but which runs rampant throughout the social sciences and general public—would have us believe there is widespread discrimination against men in most fields now).

Furthermore, this pattern is completely consistent with the idea that girls and women have different interests (Su et al) and skills (Wang et al) that lead them to prefer non-STEM careers.

Frauen studieren, nur eben lieber andere Fächer. Wenn es mit Leuten und Leben zu tun hat, dann sind sie dort weitaus eher zu finden als in Fächern, die sich damit weniger beschäftigen.

Auch interessant ist der folgende Absatz:

Surely girls and women have, historically, been discriminated against in such fields.  But discrimination in 1950 or 1970 does not constitute evidence of ongoing discrimination.  Furthermore, the evidence that girls and women prefer non-STEM fields is not an argument to avoid combating sexist discrimination where it can still be found.  Nonetheless, the list of social science victim2 groups is so long, that, most likely, almost all of us have been the target of discrimination or hostility at some point in our lives, rendering the question of whether some groups are more victimized than others muddier than it seems.

However equivocal the evidence for “bias” in the present may be as an explanation for the gender gap in STEM fields, there is ample evidence of bias. Scientific bias! Social scientists clearly „prefer“ bias explanations over other, deeply important, scientifically rigorous, social developmental evidence, such as that offered by Su et al and Wang et al.  This table reveals just how extreme this bias is:

Lee Jussim
Source: Lee Jussim

The key entry here is the citation counts in the far right.  The Moss-Racusin study is, by conventional standards, the weakest of the studies.  Its sample size is a fraction of that of the others.  It studies a relatively minor situation (hiring lab managers).  It was a single study (Su et al is a meta-analysis of scores of studies; Williams and Ceci reported five separate studies).  In contrast to Wang et al, it only studied an event at a single time point; it did not follow people’s career trajectories.

This does not make Moss-Racusin et al a “bad” study; it is merely weaker on virtually all important scientific grounds than the others.  This is not to argue that the other studies are “perfect,” either; all studies have imperfections.  But by conventional scientific standards, Su et al’s meta-analysis, the replications in Williams and Ceci, the longitudinal Wang et al study, and the far larger sample sizes in all three mean that, on most scientific methodological standards, they are superior to the Moss-Racusin et al study.

And yet, look at the citation counts.  Others are citing the Moss-Racusin et al study out the wazoo. Now, Wang et al and Williams and Ceci came out later, so probably the most useful column is the last.  Since 2015, the weaker Moss-Racusin study has been cited 50% more often than the other three combined!  That means there are probably more papers citing the Moss-Racusin et al study and completely ignoring the other three, than there are papers citing even one of the other three! What kind of „science“ are we, that so many „scientists“ can get away with so systematically ignoring relevant data in our scientific journals?

(Again, this does not make the Moss-Racusin study “bad.” The bias here reflects a far broader field problem, it does not constitute a weakness in the paper itself).

And that, gentle reader, is a gigantic scientific bias.  It might even be beyond bias. Some might call it an “obsession” with discrimination and bias so severe that it is blinding many in our field to major findings regarding gender differences that contribute to preferences for different types of fields.   

Wer eine Benachteiligung von Frauen behauptet, der wird also wesentlich häufiger zitiert als jemand, der eine Bevorzugung von Frauen behauptet. Die Benachteiligung von Frauen ist die „gewünschte Geschichte“, an rationalen Erklärungen dafür ist man nicht interessiert. Das Opfernarrativ muss eben auf jeden Fall erhalten bleiben. Ob es Frauen zusätzlich abschreckt und damit zu weniger Frauen in dem Bereich führt ist dabei wohl eher egal.

Das Schlußwort ist auch interessant:

If this analysis has any validity, the societal push to equalize gender distributions may be deeply dysfunctional, because it can succeed only by having the perverse effect of pushing people into fields they do not prefer. Of course, on moral grounds, we want to insure that all people have equal opportunities to enter any particular career.  But if there are bona fide gender differences in preferences and interests, equal opportunities may never translate into equal outcomes.

Was auch der Grund dafür ist, dass eine reine Gleichstellungspolitik eher Ungerechtigkeiten produziert, weil Unterschiede ausgeblendet werden.