Workshop on Faculty Diversity  National Data on Faculty Composition Donna Nelson, University of Oklahoma  Studies of the “Playing Field” Implicit assumptions.

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Presentation transcript:

Workshop on Faculty Diversity  National Data on Faculty Composition Donna Nelson, University of Oklahoma  Studies of the “Playing Field” Implicit assumptions are there  Personal Comments The reality of small numbers  Advice for Chairs Small things can make big improvements Marjorie Olmstead Department of Physics

Faculty Diversity Study  Donna Nelson, U. Oklahoma Chemistry  14 Fields UW-ADVANCE fields  Survey 50 top departments Ranked by research expenditures in Biased toward large depts supporting students  Faculty composition by race and gender  Compare to Ph.D. Data from NSF

Example Data: Physics Faculty UW 2002: Full Professor: 35 WM, 3 WF, 1 HM Associate Professor: 2 WM Assistant Professor: 6 WM, 1 WF UW 2004: Full Professor: 31 WM, 3 WF, 2 HM Associate Professor: 3 WM, 1 WF, 1 AM Assistant Professor: 2 WM, 1 WF 50 Departments: 1,988 Faculty 132 Women (6.6%); 263 Minorities (13%); 31 Women of Color (1.5%) Full Professor: 1207/61 WM/WF; 6/0 BM/BF; 19/3 HM/HF; 124/11 AM/AF; 1/0 NAM/NAF Assoc. Professor: 207/21 WM/WF; 2/0 BM/BF; 6/0 HM/HF; 35/6 AM/AF; 0/0 NAM/NAF Asst. Professor: 190/19 WM/WF; 4/0 BM/BF; 5/5 HM/HF; 40/6 AM/AF; 0/0 NAM/NAF Women of Color Hidden in Statistics

10 Fields, 500 Departments Total: 13,796 Total: 3,044 Total: 179 (1.3%) Total: 136 (4.5%)

Representation Declines with Rank  Women nearly HALF as likely to become professors  Few Blacks, No Native Americans  Foreign Asians and Hispanics increase ratio: US born still underrepresented on faculty Bio MPS Eng All Men 1.17 All Women 0.63

Why so Few?  MYTH: “It’s THEIR fault -- women just don’t apply.”  REALITY: “My grad school experience was so awful I just want to get out of there.”  Example of Change: Medical Schools after Title IX 1972 Law -- Education Gender Discrimination Made Illegal Parity in 30 years 50% 10% Widely Practiced 10% Quota Matriculation rate = Applicant rate

Impact Rating Score  Large body of research shows: Implicit Assumptions Impact Evaluation  Gender Bias and Research Papers Paludi and Bauer (Sex Roles, 1983) Tilted Playing Field Reviewer (1-5, 1 top) John T. McKay Joan T. McKay J. T. McKay Male Female  Gender Bias and Performance Evaluation Orchestra tryouts behind curtain Stereotype threat on exam performance  = 3 Nature Papers  Gender Bias and Post-Doc Applications Wenerås and Wold (Nature, 1997)

(Implicit) Discrimination  Lower expectations  Uneven evaluation  Narrow view of excellence  Exclusion from informal networks  Other people feel uncomfortable  Accumulation of Disadvantage

Personal Observations  Small numbers mean everybody counts UW Physics nearly lost 60% of women in one quarter Physics PhDs years*50 departments: 8,261 total — 2 Native American Women —21 Black Women —31 Hispanic American Women  Each person must consciously confront their implicit assumptions Grew up in 99 % white suburb Adult before I knew professional, educated minorities  Scientific and educational enterprise requires trust Different cultural expectations must be dealt with head on

Good Chairs Make a Difference  Take ownership of the “problem” to create a public, inclusive climate for students and faculty  Consciously and publicly counter implicit assumptions and accumulated disadvantage  Set transparent and inclusive criteria and processes for hiring, promotion, salary and resources.  Give women and minorities assignments to gain leadership skills (both scientific and administrative)  Have all faculty actively mentor and recruit minority students to the profession. One more/year is significant.  Compare attitudes of 1st and 5th year grad students -- do they still want to be academics? Is there a gender and/or racial difference in the response? Find out WHY.