What guy wouldn't want it? Male victimization experiences with female‐perpetrated stranger sexual harassment
Bibliographic record
Abstract
Abstract The present research explored female‐perpetrated stranger sexual harassment of young male victims. Across two studies, male participants aged 16–23 reported that they had experienced a range of unwanted sexual attention from unknown female perpetrators, including both in‐person harassment (e.g., seductive behavior and catcalls, unwanted sexual touching) and online harassment (e.g., unsolicited sexual text messages and images, requests for nude self photos). Participants reported that in‐person sexual harassment started as early as 9–12 years of age and online harassment began between 12–14 years of age. Open‐ended descriptions of these early events revealed troubling narratives of non‐consensual sexual touching, forcibly removed clothing, groping, aggression, and being followed, with much of it committed by adult women. Participants recounted being asked, in adolescence, to send nude photos and receiving persistent sexual demands, often from older women. In addition, participants reported uncertainty with gender role expectations, believing that they were supposed to enjoy sexual attention but in reality finding it disturbing and unpleasant. Practical implications, policy recommendations, and future directions are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".