Perception of Partner Abuse and Its Impact on Marital Violence from Both Spouses
Bibliographic record
Abstract
Few studies have investigated bi-directional models of marital violence. Research suggests that female victims are also often perpetrators of violence. Accordingly, some researchers propose that we should test the hypothesis that the victim and perpetrator roles can be played by both men and women. The current study addresses this issue by attempting to understand the effect that perceptions of spousal violence will have on both partners’ level of marital violence. Our objectives were to verify the links between levels of violence and perceptions of violence by both partners, and actual self-reports of each type of violence perpetrated. We verified if self-reports and partner’s reports of violence would differ, if one partner’s abuses would influence the other partner’s abuses, and whether the spouse’s self-reported violence or the other spouse’s perception of that violence had a differential impact on the level of violence perpetrated. Twenty-three couples in which the male partner was undergoing treatment for marital violence took part in the study. Results indicate that for both partners perceptions of partner violence modulate the level of marital violence that is perpetrated. The link between perceptions and violent behaviors appears to explain female marital violence better than that it does for males. Implications based on these results are discussed.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".