Discrimination starts even before grad school, study finds

For those interested in what the text of the email was, from the study [1]:<p>-----<p>Subject Line: Prospective Doctoral Student (On Campus Today&#x2F;[Next Monday])<p>Dear Professor [Surname of Professor Inserted Here],<p>I am writing you because I am a prospective doctoral student with considerable interest in your research. My plan is to apply to doctoral programs this coming fall, and I am eager to learn as much as I can about research opportunities in the meantime.<p>I will be on campus today&#x2F;[next Monday], and although I know it is short notice, I was wondering if you might have 10 minutes when you would be willing to meet with me to briefly talk about your work and any possible opportunities for me to get involved in your research. Any time that would be convenient for you would be fine with me, as meeting with you is my first priority during this campus visit.<p>Thank you in advance for your consideration.<p>Sincerely, [Student’s Full Name Inserted Here]<p>[1] <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2063742" rel="nofollow">http:&#x2F;&#x2F;papers.ssrn.com&#x2F;sol3&#x2F;papers.cfm?abstract_id=2063742</a>

In the main plot in the paper they plot the absolute differences in response rates as opposed to plotting relative differences. 90% vs 80% for a given department seems drastically different from 5% vs 15%, but those would get equal value bars. I also tend to have a knee-jerk negative reaction to the phrase &quot;reverse-discrimination&quot; as it evokes the &quot;you can&#x27;t be sexist against men!&quot; etc. type of ideas. This sort of feeling is enhanced by the splitting of the data set into white males and non-white&#x2F;non-males for significant parts of the analysis.<p>It&#x27;s interesting that they didn&#x27;t find that percentage minorities or women on faculty had significant impact on response rates (with the exception of small results for asian-asian support). That seems to suggest that any discriminatory tendency is fairly uniform across individuals, reinforcing that policy changes to reduced discrimination must be collective effort.<p>Otherwise this seems like a good experiment and a good paper. The email thing just seems really clean and a realistic depicter of reality. Let&#x27;s see if there are any significant changes after peer review, I suppose.

&gt; Most would acknowledge that women and minorities already face more hurdles in academia than their white, male peers. A lack of mentors, occasionally overt discrimination and the academy’s poor work-life balance, are well-documented issues.<p>OK, I&#x27;m stumped. I can&#x27;t figure out how poor work-life balance is more of a hurdle for women and minorities than for white males. Anyone know?

When I was a physics undergrad all 12 physics majors in my graduating class were given a &quot;talk&quot; by a senior white faculty member about the &quot;realities&quot; of the physics pyramid. Basically we were told that our Chinese competitors made much better grad students and we should basically not bother applying to grad school because we couldn&#x27;t compete against the &quot;better&quot; Chinese.

I worked at a dept with ~2.5% acceptance rate.<p>There were two, subtle factors in play: soft ageism and PI&#x27;s hiring their clones (in thought, gender and race).<p>So the dept gravitated to predominantly two minorities, at least in terms of staff, faculty, visiting researchers and grad students.

EDIT: With regard to what I pointed out in my original comment (below for reference), I believe the Nature article chart is in error. Reading the actual paper (p.55), the gap in &quot;engineering and computer sciences&quot; (while smaller than some other fields) is actually <i>more</i> statistically significant than some of the other results. I suppose that may be because they were able to trial more professors.<p>I guess that means, be careful trusting charts, even from <i>Nature</i>&#x27;s blog. The smaller gap may still count for something compared to a few worse fields, but it&#x27;s not among the statistically-unclear results of the fields studied.<p>--original below--<p>From the graph, &quot;engineering and computer sciences&quot; was one of the smallest measured gaps, and further didn&#x27;t feature the &quot;*&quot; which indicated a statistically-significant result.<p>So in all the often-justified criticism, the fact that CS&#x2F;engineering are better than many other fields-of-practice should count for something.<p>(Interestingly also per the chart, in &#x27;Fine Arts&#x27; the faculty discrimination ran in favor of women&#x2F;minorities, to a statistically-significant level.)

Peculiar why women were bunched together with minorities. I wonder how the results would look if they were decoupled. I also wonder what their reasoning was in attributing the small effect linking different names to differing response rates to &#x27;implicit bias&#x27; was.

How many American women receive selective service notification?<p><a href="https://www.sss.gov/" rel="nofollow">https:&#x2F;&#x2F;www.sss.gov&#x2F;</a>

some of the differences aren&#x27;t statistically significant?

No shit?

What&#x27;s the relative proportion of men in prison to women?