Backlash against the #MeToo movement: How women’s voice causes men to feel victimized
Bibliographic record
Abstract
Three studies examined whether perceived increase in women’s “voice” (i.e., being heard and taken seriously about sexual assault) contributes to perceptions of bias against men. In Study 1, both men and women who perceived women to have a greater voice related to sexual assault, perceived greater victimization of men. This relationship was stronger for relatively conservative participants. In Study 2, relatively conservative (but not relatively liberal) participants who read about #MeToo perceived greater men’s victimization than those in the control condition. Study 3 examined responses to perceiving that men are victimized by #MeToo. For relatively conservative (but not liberal) men, perceptions of men’s victimhood led to less willingness to work alone with a woman and less willingness to combat sexual assault (relative to a control condition). Thus, while the #MeToo movement brings awareness of issues of sexual assault, it also generates a backlash among the more conservative, and may accentuate gender disparities.
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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 | 0.002 | 0.008 |
| 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 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".