Double Victimization in the Workplace: Why Observers Condemn Passive Victims of Sexual Harassment
Bibliographic record
Abstract
Five studies explore observers' condemnation of passive victims. Studies 1 and 2 examine the role of observers' behavioral forecasts in condemning passive victims of sexual harassment. Observers generally predicted that they would engage in greater confrontation than victims typically do. More importantly, the more confrontation participants predicted they would engage in, the more they condemned the passive victim, and the less willing they were to recommend the victim for a job and to work with her. Study 3 identifies the failure to consider important motivations likely experienced by victims—and that contribute to their passivity—as an important driver of behavioral forecasting errors. Having forecasters reflect on motivations normally experienced but not typically forecast produced behavioral predictions that were more consistent with the actual passive behavior of sexual harassment victims. Studies 4 and 5 reduce condemnation of passive sexual harassment victims by highlighting important motivations likely experienced by those victims (Study 4) and by having participants recall a past experience of not acting when being intimidated in the workplace, a situation related but distinct from sexual harassment (Study 5). The results from these studies add insights into the causes and consequences of victim condemnation and help explain why passivity in the face of harassment—the predominant response—is subject to so much scorn.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".