Gender symmetry in partner violence: Evidence and implications for prevention and treatment.
Bibliographic record
Abstract
Physical aggression against marital partners, although long recognized and deplored, has historically been denied under the guise of protecting the privacy and integrity of the family (Calvert, 1974). The training manual published by the International Society of Chiefs of Police (International Association of chiefs of Police, 1967), for example, advised officers to minimize involvement in what were then called “domestic disturbances.” Some cities in the United States followed an informal “stitch rule” under which arrests were made only if there was a wound that required sutures. As a result of efforts by the women’s movement starting in the mid 1970s there has been a reversal of these traditional approaches. In most jurisdictions in the US and Canada, police are now required or advised to arrest perpetrators of physical attacks on a partner. Concordant with the arrest policy has been the growth of treatment programs for perpetrators. Many courts now offer participation in such programs as an alternative to incarceration. There may be about a thousand such programs in operation in the United States, and over 200 in Canada (National Clearinghouse on Family Violence, 2004). These are tremendous advances, but there is also evidence questioning the effectiveness of the 30 year long effort to reduce “domestic violence.” A central point of this chapter is that the effort has been handicapped by conceptualizing physical assaults on a
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".