Anxious and Hostile: Consequences of Anxious Adult Attachment in Predicting Male-Perpetrated Sexual Assault
Bibliographic record
Abstract
Attachment theory has increasingly been utilized to understand the etiology of sexual violence, and anxious attachment appears to be especially informative in this domain. We investigate the influence of general anxious attachment and specific anxious attachment on hostile masculine attitudes to predict male-perpetrated sexual assault. We hypothesize that hostile masculinity will mediate the relationship between general anxious attachment style and sexual assault perpetration (Hypothesis 1) and the relationship between specific anxious attachment to the assaulted woman and sexual assault perpetration (Hypothesis 2). Men ( N = 193) completed the Sexual Experiences Survey (SES) to determine sexual assault history and completed measures of general attachment style, specific attachment to the woman involved in the sexual activity, and measures of hostile masculine attitudes. Results support the hypothesized mediation models, such that general anxious attachment and specific anxious attachment are significantly associated with hostile masculinity, which in turn, predicts the likelihood of male-perpetrated sexual assault. The findings suggest that the unique characteristics of anxious attachment may escalate into hostile masculinity, which then increases the likelihood of sexual assault perpetration. This research is the first to investigate attachment bonds to the woman involved in the sexual activity and likelihood of sexual assault perpetration against the same woman.
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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 | 0.001 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".