The effects of affirmative action on perceptions of women entering male dominated academic programs
Bibliographic record
Abstract
With the enactment of affirmative action policies and the increase of women in traditionally male dominated areas of work and study, it is important to investigate how people perceive women selected under affirmative action in male sex-typed academic fields. One hundred and fifty-seven undergraduates, 112 females and 45 males, reviewed an application package of a male or female student who was accepted to either an Engineering (strongly male sex-typed) or Dentistry (slightly male sex-typed) program at a university that was or was not committed to an affirmative action policy. Participants rated the applicant on measures of perceived competence, interpersonal, activity, and potency characteristics; projected program progress; and the perceived role of qualifications and fairness of the application process. Consistent with the gender stereotyping hypotheses, female applica ts were perceived similarly to male applicants in the Dentistry program. Unexpectedly, however, female applicants were also perceived similarly to male applicants in the Engineering program. Contrary to the discounting hypotheses, female applicants associated with affirmative action were perceived just as favorably as applicants not associated with such policies. Discounting of the affirmative action recipients' qualifications was not evident, and the presence of the policy did not affect perceptions of the fairness of the decision process. Overall, female applicants accepted under affirmative action into male sex-typed academics were not discriminated against based on either their gender or the affirmative action label.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".