How Does it Feel? Factors Predicting Emotions and Perceptions Towards Sexual Harassment
Bibliographic record
Abstract
The present research addresses the effect of different variables on emotions and on perceptions held by research participants vis-à-vis situations that could be construed as sexual harassment. A total of 833 Israeli students participated in the study and use was made of a Sexual Harassment Definition Questionnaire (SHDQ). It was found that emotions aroused due to behaviour that is perceived as sexual harassment depend on variables such as age and gender of perpetrator and victim. More negative feelings were identified towards behaviours suggestive of sexual harassment among women than men, and among younger than older individuals, principally in situations where the perpetrator is a man. Situations in which the perpetrator and the victim were of the same gender were experienced as less flattering than those in which the gender was different. It was also found that women above the age of 40 perceived behaviour in which a woman related to a man or a woman in sexual terms as a situation with a higher potential for sexual harassment than one in which a man related to a woman or a man in a similar way. The findings show the extent to which social perceptions and emotions relating to sexual harassment are dynamic and context-dependent.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".