Identifying Risk Factors of Gender-Based Harassment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gender-based harassment is a common and damaging form of gender-based violence that encompasses a wide range of behaviors (e.g. cat calling, using sexist language, using homophobic slurs, etc.). Despite its negative impact on victim-survivors, very few studies have identified risk factors associated with the perpetration of gender-based harassment. In the current study, we examined whether well-established risk factors for in-person sexual offending were also predictive of gender-based harassment behaviors. Participants (N = 1,200) were recruited via Qualtrics’ research panels and completed measures assessing potential risk factors (i.e. younger age, sexual deviancy, general antisociality) and whether they had perpetrated gender-based harassment in the preceding twelve months. Overall, 14.0% of the sample reported perpetrating gender-based harassment. All factors independently predicted harassment perpetration. When considered within a multivariate model, being a man, being younger in age, and scoring higher on hypersexuality and anger were significantly predictive of gender-based harassment when controlling for all other variables. Results suggest that established risk factors for in-person sexual offending, including elements of sexual deviancy and general antisociality, might also be important in predicting the perpetration of gender-based harassment, and may therefore serve as potential treatment targets.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it