Keep it Simple: Using the BARR-2002R to Assess Risk for Violent Recidivism in a Police Context
Bibliographic record
Abstract
The general criminality subscale and age at release item from the Static-2002R form the actuarial risk scale, Brief Assessment of Recidivism Risk-2002R (BARR-2002R), to predict violent and general recidivism among male sex offenders. Previous studies show that the BARR-2002R predicts violent and general recidivism significantly better than the Static-99R and Static-2002R with convicted sex offenders. Although the BARR-2002R, the Static-2002R, and its predecessor, the Static-99R, have been validated using convicted offender samples, only one recently published study has examined the predictive validity of the Static-99R and Static-2002R with non-convicted male perpetrators of sexual assault. The present study investigated the ability of the BARR-2002R to predict future violent offending of 290 perpetrators that were randomly sampled from police-referred sexual assault cases. Police cases were retrospectively coded for the Static-99R and Static-2002R, which includes the six items that comprise the BARR-2002R. Provincial and federal criminal records were used to code recidivism outcomes including presence, severity, frequency and imminence of future violence. The relationship between BARR-2002R scores and recidivism outcomes as well as the utility of the BARR-2002R as a risk tool for police officers who investigate sexual assault had not yet been examined. Results indicate that the BARR-2002R had very good predictive validity for future violent and general recidivism and higher scores were associated with increased frequency of post-index violent offences and decreased time until violent reoffending, though severity of future violence was not related to higher BARR-2002R scores. Implications for the use of the BARR-2002R in front-line work and directions for future research will be discussed. Discipline: Psychology Honours Faculty Mentor: Dr. Sandy Jung
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".