Women in Science poll responses

Mark Camara Mark.Camara at Helsinki.fi
Fri Sep 27 16:00:12 EST 1996


As a male evolutionary biologist who normally merely lurks on the edges
of this newsgroup, it may be inappropriate to add my views to this
particular discussion, but I hope they will be tolerated.

JuneKK recently summarized the results of an opinion poll of female
neuroscientists and concluded based on the responses that there were
still important but "subtle" problems facing women who choose scientific
careers.  These conclusions, however, are based on a very small sample
of approximately 30 responses gathered in an entirely non-random
survey.  The biases in response probablilites are, I would argue,
probably heavily in favor of those women who feel that they have
experienced discrimination, and yet, the number reporting that they have
been discrimainated against never exceeds 50%.  Furthermore, the poll
asked about the perceptions of respondees, and it is oversimplified to
equate these perceptions to actual discrimination. (Would JuneKK give
equal weight to the perceptions of men having trouble finding jobs who
feel that they are victims of affirmative action?).

While I do not want to deny that there have been and still are
gender-based problems in the academic community, I would like to caution
against using such non-scientific sources of "evidence" to either fan
the flames of a partially extinguished fire or to maintain the walls
between men and women in science when we should all be working to tear
them down.  With all due respect and at the risk of being flamed, I
suggest that the best way for women to succeed in science and to gain
equal footing with men is to try to de-emphasize gender in all important
aspects of scientific endeavor, to be the best scientists they can be 
by devoting time to their work rather than to opinion polls, to be as
fair as human nature allows when facing any gender-based biases in
either direction, and to stand up to INDIVIDUAL transgressions and
transgressors whenever and wherever they appear rather than resorting to
statistical arguments based on biased samples.



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