Young Women’s Experiences With Technology-Facilitated Sexual Violence From Male Strangers
Bibliographic record
Abstract
Stranger-perpetrated harassment was identified decades ago to describe the pervasive, unwanted sexual attention women experience in public spaces. This form of harassment, which has evolved in the modern era, targets women as they navigate online spaces, social media, texting, and online gaming. The present research explored university-aged women's experiences (n = 381) with online male-perpetrated sexual harassment, including the nature and frequency of the harassment, how women responded to the harassment, and how men reportedly reacted to women's strategies. Trends in harassment experiences are explored descriptively and with thematic analysis. Most women reported receiving sexually inappropriate messages (84%, n = 318), sexist remarks or comments (74%, n = 281), seductive behavior or come-ons (70%, n = 265), or unwanted sexual attention (64%, n = 245) in an online platform, social media account, email, or text message. This sexual attention from unknown males often began at a very young age (12-14 years). The harassment took many forms, including inappropriate sexual comments on social media posts, explicit photos of male genitalia, and solicitations for sex. Although most women reported strong negative emotional reactions to the harassment (disgust, fear, anger), they generally adopted non-confrontational strategies to deal with the harassment, electing to ignore/delete the content or blocking the offender. Women reported that some men nevertheless persisted with the harassment, following them across multiple sites online, escalating in intensity and severity, and leading some women to delete their own social media accounts. These results suggest the need for early intervention and education programs and industry response.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".