Examining Positive and Negative Reactions and Conciliatory Behaviors After Partner Violence Perpetration
Bibliographic record
Abstract
This study investigated positive and negative reactions and conciliatory behaviors after perpetration of intimate partner violence (IPV). The goals were to examine the rates of these reactions and their associations with key attitudinal and personality factors. During program intake at a community agency, 172 partner violent men completed assessments of positive reactions (e.g., feeling justified) and negative reactions (e.g., feeling ashamed) after IPV, conciliatory behaviors after IPV (e.g., buying flowers for the partner), frequency of physical assault and abuse perpetration, and motivational readiness to change. In addition, a subset of participants ( n = 64-71) completed assessments of outcome expectancies of IPV and borderline, antisocial, and psychopathic personality characteristics. The vast majority of participants (89.8%) reported negative reaction(s) after IPV; 32.7% reported positive reaction(s), and 67.5% reported conciliatory behavior(s). Positive reactions after IPV were associated with positive outcome expectancies of IPV, more frequent abuse perpetration, and antisocial features. Negative reactions after IPV were associated with greater motivation to change, more frequent abuse perpetration, and borderline features, and were inversely linked to psychopathic traits. Conciliatory behaviors were associated with motivation to change, borderline characteristics, and lower levels of psychopathic traits. Cognitive, emotional, and behavioral reactions to IPV may be important for stimulating clinical discussion of motivations and barriers to change, and can inform the functional analysis of IPV.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".