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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".