An examination of the Danger Assessment as a victim-based risk assessment instrument for lethal intimate partner violence.
Bibliographic record
Abstract
Research on intimate partner violence (IPV) has led to the development of more than a dozen specialized risk assessment instruments. The present study evaluates the Danger Assessment (DA; Campbell, 1986; Campbell et al., 2003), which was designed to assess risk for lethal IPV based on victim information. We completed DA ratings for 100 male offenders convicted of IPV related crimes. Follow-up data on the presence of lethal or near-lethal IPV were obtained on average 5.19 years after assessment. Findings indicated that DA ratings varied as a function of the sources of information relied upon; ratings were lower when based on victim reports alone, compared with victim reports and other information. The DA also appeared to yield unrealistically high estimates of risk for lethal IPV. Although more than half of the sample achieved the highest DA ratings, none of the offenders was convicted of lethal or near-lethal IPV during the follow-up period. Findings call into question the utility of the DA.
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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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".