Diversity@SPP

Archive for the ‘resources’ Category

SPP 2009 Conference

Posted by anonfemphil on June 4, 2009

There will be a lunch time discussion of diversity and the SPP on June 13th; Virginia Valian, whose work on diversity is very highly regarded, will be joining us.  Everyone at the conference is welcome to come. 

Below you’ll find info about the lunch and three links to new news about diversity.  This blog is also the repository for a number of posts about diversity and even more links to other resources.

Time and location

June 13; 1:15 to 2:45.

State Room East, on the second floor of the IMU (the main conference building)

 
Links:
 
Diversity statistics for the 2009 SPP conference.  (Many thanks to the program chairs).
 
…data from several studies indicate that greater male variability with respect to mathematics is not ubiquitous. Rather, its presence correlates with several measures of gender inequality. Thus, it is largely an artifact of changeable sociocultural factors, not immutable, innate biological differences between the sexes.
(Whatever you think of Sotomayor, it is interesting to see commentary that could be drawn from a handbook on racist and sexist stereotypes.  In these terms, assertive women are bullies and Latinas are sloppy and not too bright.)  H/T to this post.
 
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Hiring for 09? Some diversity advice:

Posted by anonfemphil on December 22, 2008

From Jender at FeministPhilosophers:

As we’ve noted before, there’s a lot of evidence suggesting that nearly everyone is subject to unconscious or implicit bias, and that these biases can have an inappropriate impact on hiring decisions. For example (one among many),

In a national study, 238 academic psychologists (118 male, 120 female) evaluated a curriculum vitae randomly assigned a male or a female name. Both male and female participants gave the male applicant better evaluations for teaching, research, and service experience and were more likely to hire the male than the female applicant

So if you’re hiring this year, you may want to think about how to keep this from happening to you. Here are a few suggestions:

1. Learn about and discuss research on biases and assumptions and consciously strive to minimize their influence on your evaluation. Experimental studies show that greater awareness of discrepancies between the ideals of impartiality and actual performance, together with strong internal motivations to respond without prejudice, effectively reduces prejudicial behavior.

2. Develop evaluation criteria prior to evaluating candidates and apply them consistently to all applicants. Research shows that different standards may be used to evaluate male and female applicants and that when criteria are not clearly articulated before reviewing candidates evaluators may shift or emphasize criteria that favor candidates from well-represented demographic groups.

3. Spend sufficient time (at least 20 minutes) evaluating each applicant. Evaluators who were busy, distracted by other tasks, and under time pressure gave women lower ratings than men for the same written evaluation of job performance. Sex bias decreased when they were able to give all their time and attention totheir judgments, which rarely occurs in actual work settings.

4. Be able to defend every decision for eliminating or advancing a candidate. Research shows that holding evaluators to high standards of accountability for the fairness of their evaluation reduces the influence of bias and assumptions.

5. Periodically evaluate your judgments, determine whether qualified women and underrepresented minorities are included in your pool, and consider whether evaluation biases and assumptions are influencing your decisions by asking yourself the following questions:

a. Are women and minority candidates subject to different expectations in areas such as numbers of publications, name recognition, or personal acquaintance with a committee member?
b. Have the accomplishments, ideas, and findings of women or minority candidates been undervalued or unfairly attributed to a research director or collaborators despite contrary evidence in publications or letters of reference?
c. Are assumptions about possible family responsibilities and their effect on a candidate’s career path negatively influencing evaluation of a candidate’s merit, despite evidence of productivity?

All of the above suggestions are taken from an excellent brochure that Alphafeminist called to our attention, which can be found in its very excellent entirety here. (And there are many more suggestions, and a lot more data, there.)

I think most departments genuinely do want to increase their hiring of women and minorities. But I also think that implicit bias may be impeding these efforts. If I’m right about the former, then departments might want to entertain the possibility that implicit bias is playing this role. And they should be glad to have some suggestions about how to take action against it. With that in mind, I urge you to pass some of this information on to friends and colleagues involved in hiring even if you’re not involved yourself.
———-
Update: As AZ notes in comments, departments should also be careful about weighting pedigree too heavily. If someone comes from a less prestigious pedigree, and has held less research-friendly jobs, but has *nonetheless* managed to get a damned good publication record, surely this is a sign that they will do even better in a more salubrious environment. Such candidates should be viewed as potentially especially promising, rather than getting passed over.

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Research on the Implicit Association Test

Posted by anonfemphil on November 20, 2008

Before you read this, you may want to look at earlier posts on implicit bias.  The first  is here.

Some positive:

“Implicit Association: Validity Debates.” A collection of research, compiled by A. Greenwald.
“The “Implicit Association Test at age 7: A methodological and conceptual review.” B. Nosek, A.G. Greenwald, M.R. Banaji. (In J. A. Bargh, ed., Automatic processes in social thinking and behavior. Psychology Press. 2007)

“Understanding and Using the Implicit Association Test III.” A meta-analysis of more than 100 studies that concludes the IAT has predictive validity. A. G. Greenwald, T. A. Poehlman, E. L. Uhlmann, M. R. Banaji. (In press, Journal of Personality and Social Psychology.)

“IAT Studies Showing Validity with ‘Real-World’ Subject Populations.” Collection of 20 reports.

Some more negatively critical:

“Unconscious Racism: A Concept in Pursuit of a Measure.” A critique of IAT and unconscious bias theory. H. Blanton, J. Jaccard. (Annual Review of Sociology, 2008.)

“Strong Claims and Weak Evidence: Reassessing the Predictive Validity of the IAT.” H. Blanton, J. Jaccard, J. Klick, B. Mellers, G. Mitchell, P.E. Tetlock. (In press, Journal of Applied Psychology.)

“Ten Frequently Asked Questions about Implicit Measures and Their Frequently Supposed, But Not Entirely Correct Answers.”
A look at the strengths and weaknesses of the IAT and other implicit measures. B. Gawronski. (In press, Canadian Psychology.)

Readings compiled in the NY Times.

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Some Resources

Posted by anonfemphil on October 18, 2008

  1.  The website for the National Science Foundation’s Advance Program, which aims to improve the representation of women in the STEM disciplines (Science, Technology, Engineering and Mathematics).
  2. A excellent and informative pamphlet drawn up by philosophers and scientists about the causes of the lack of diversity and about remedial measures:  go here.
  3. Sally Haslanger’s well-known paper on changing the ideology and culture of philosophy; it’s here.
  4. From England’s Wellcome Trust; possibly defensive reaction to the finding on gender discrimination and peer review in Sweden.
  5. Reflections on the evalution of men and women by a Stanford biologist who underwent a female to male sex transformation: here and, for a report on a related panel, here.
  6. Tutorials for change on gender schemas and science:  slides shows on the effects of gender discrimination and an hypothesis to explain it by Virginia Valian.  Her work, particular her book, Why so Slow?, forms a classic resource.
  7. Unlocking the Clubhouse, an account of Carnegie Mellon’s diagnosis of thecauses of the lack of women in their computer science graduate program and their successful steps in changing it.
  8. Athena Unbound:  has some data about the international position of women in academia, including an explanation of why some muslim counntries look more favorably on women in science  than the US does.
  9. From the University of Michigan’s Advance website:

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