The relationship between empathy, clinical problems, and reoffending in a sample of Canadian male offenders
Bibliographic record
Abstract
This chapter presents findings from an analysis of male offenders serving custodial sentences at the Ontario Correctional Institute (OCI) – a provincial correctional treatment centre in Ontario, Canada. The aim of this study was to understand the relationship between Basic Empathy Scale (BES) scores and other psychological and criminogenic risk-need factors measured at admission and discharge via standardised clinical measures and official criminal offending records. The sample included 1,028 individuals assessed at admission, 461 at discharge, and a subset of 253 cases for whom two-year recidivism status could be calculated. Results indicated that BES scores were negatively associated with antisocial variables (e.g. previous convictions, antisocial thinking) and positively associated with mood disorders (e.g. depression, anxiety). There were significant improvements in BES scores from admission to discharge, but these were generally higher for sex offenders who had higher levels of empathy than non-sex offenders at both time points. For sex offenders only, total and cognitive empathy change scores predicted two-year recidivism status, even when controlling for confounding variables. Overall, the findings point to the importance of empathy as a correctional treatment target, however, more research is needed to demonstrate this for individuals who do not commit sexual crimes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".