Why Do People Perpetrate Sexual Harassment in Public Space? A Systematic Scoping Review
Bibliographic record
Abstract
Despite the growing scholarly interest in sexual harassment in public space over the past decades, there is still no systematic overview of explanations for why perpetrators engage in it. Such an overview would be valuable for improving the effectiveness of prevention strategies. Hence, this review was guided by the following questions: (1) What explanations are provided in the literature for why people engage in sexual harassment in public space, and (2) have these explanations been studied empirically? 12 databases were searched for relevant studies across disciplines. Of the 4,300 studies identified, 29 met the inclusion criteria. A thematic analysis was conducted to categorize the explanations according to the social ecological framework. We identified 10 themes across four levels: (1) individual – personality traits, psychosocial capacities, and behavioral tendencies; (2) relationship – communicative motivations, peer dynamics, and family dynamics; (3) community – socio-spatial environment and structural inequalities; (4) societal – social norms and structural inequalities. We demonstrate that the literature most notably provides empirically substantiated explanations at the first two levels. Community- and societal-level factors, and their interplay with individual- and relationship-level factors, require more thorough empirical scrutiny. Our findings, moreover, suggest that: (a) efforts to reduce sexual harassment in public space should rather focus on peer groups than (potentially) perpetrating individuals; (b) such efforts should focus on addressing group dynamics, perpetrators’ psychosocial capacities, and the social and gender norms that shape their worldviews, besides continuing to raise awareness of what sexual harassment is and how it is experienced by targets.
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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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".