Empowered by Adversity? Exit, Voice, and Silence in the Aftermath of Gender Discrimination at Work
Bibliographic record
Abstract
Social psychological research suggests that workplace discrimination harms women’s self-confidence and mental health, which may lead them to remain silent or quit their jobs after facing discrimination. However, feminist scholarship argues that discrimination can generate feminist consciousness and resistance. To interrogate these conflicting expectations, we draw on in-depth interviews with professional women to examine exit, voice, and silence in discrimination’s aftermath. We find that some women remain silent or exit organizations in search of less hostile environments. Others, however, develop feminist consciousness, voice complaints, and sometimes accomplish hard-fought changes within their organizations. To explain these divergent responses, we identify support networks as a crucial mechanism. Support networks help women avoid self-blame and rumination by resolving the ambiguity that frequently obscures discrimination. Support networks also spread awareness of discrimination and generate feminist solidarity. In doing so, they encourage women to contest negative treatment by exercising voice. Implications for the study of workplace discrimination, the debate over the stalled gender revolution, and occupational segregation are discussed.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".