Reminders of Past Injustices Against Women Undermine Support for Workplace Policies Promoting Women
Bibliographic record
Abstract
Understanding past injustices endured by traditionally disadvantaged groups in our society (i.e., women, racial minorities) is crucial for understanding inequality today and, moreover, such injustices are commonly used as a rationale for contemporary equality policies. In this paper, we draw on the literatures on social identity, history and identity, and employment equity, to suggest that reminding people about social injustice and discrimination against a disadvantaged group (e.g., women) can invoke social identity threat among advantaged group members (e.g., men) and consequently undermine support for contemporary employment equity (EE) policies by fostering the belief that inequality no longer exists. We find support for our hypotheses in three studies in the context of Canadian (Study 1 and 3) and American (Study 2) EE policies. In a sample of student job applicants (Study 1) and employees (Study 2), we found that past reminders of injustice toward women undermine men’s support for a gender-based EE policy due to denial of current gender discrimination, whereas they do not undermine women’s support for EE policies. In Study 3 we found that providing additional evidence about the advancement of women’s right mitigates men’s negative reactions to EE policy by deflecting threat to their social identity.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".