Minimizing the Pervasiveness of Women's Personal Experiences of Gender Discrimination
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Given the Rejection-Identification Model ( Branscombe, Schmitt, & Harvey, 1999 ), which shows that perceiving discrimination to be pervasive is a negative experience, it was suggested that there would be conditions under which women would instead minimize the pervasiveness of discrimination. Study 1 ( N = 91) showed that when women envisioned themselves in a situation of academic discrimination, they defined it as pervasive, but when they experienced a similar laboratory simulation of academic discrimination, its pervasiveness was minimized. Study 2 ( N = 159) showed that women who envisioned themselves experiencing discrimination minimized its pervasiveness more so than women reading about discrimination happening to someone else. Further, mediation analysis showed that minimizing the pervasiveness enhanced positive affect about personal discrimination. Implications for minimizing on both an individual and social level are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it