Men’s differential identification with female-perpetrated intimate partner victimization
Bibliographic record
Abstract
Men are often reluctant to self-identify as victims of female-perpetrated intimate partner violence (f-IPV), despite significant harms. This reluctance results in underreporting of experiences and a concomitant lack of support and resources for male victims. We examined predictors of men’s differential self-identification as victims of f-IPV; that is, between men who self-identify both as having experienced and as being a victim of f-IPV (abuse and victim identified = AVI), men who self-identify as having experienced f-IPV, but not as being a victim of f-IPV (abuse-only identified = AI), and men who self-identify as neither having experienced nor being a victim of f-IPV, despite behaviourally having experienced it (non-abuse and non-victim identified = N-AVI). We recruited cisgender men (N = 212) to an online study examining experiences of f-IPV and identification with abuse. About two-thirds of our sample did not self-identify as victims of f-IPV despite reporting victimisation experiences. We found that frequency of f-IPV, psychological vulnerability from f-IPV, precarious manhood beliefs, and ambivalent sexism significantly predicted men’s self-identification as victims of f-IPV. We elucidate predictors of men’s reluctance to self-identify as victims of f-IPV, allowing for the identification of men who may be less likely to seek and obtain support.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".