The Prediction of Recidivism with Aboriginal Offenders: A Theoretically Informed Meta-Analysis
Bibliographic record
Abstract
Research has established eight theoretically based central risk/need factors predictive of recidivism; however, there is little research examining the applicability of these risk factors to Aboriginal offenders. A meta-analysis was undertaken to examine whether (1) criminal history, (2) pro-criminal attitudes, (3) pro-criminal associates, (4) antisocial personality pattern, (5) employment/education, (6) family/marital, (7) substance abuse, and (8) leisure/recreation are applicable to Aboriginal offenders and whether these factors predict recidivism equally well for this group as they do for non-Aboriginal offenders. Thirty-two reports/articles and 12 data sets were reviewed which yielded 49 independent samples producing 1,908 effect sizes. Using both random and fixed effects analyses, results indicated that all of the central eight risk/need factors were predictive of general and violent recidivism for Aboriginal offenders; however, some factors predicted significantly better for non-Aboriginal offenders. This review also examined other factors (e.g., history of victimization and emotional factors) and there was an attempt to evaluate Aboriginal-specific risk factors (e.g., cultural identity) but no empirical studies existed on the latter. Limitations and future directions are discussed, but overall, the results support the position that the central eight risk factors are valid predictors of recidivism for Aboriginal offenders.
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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 | 0.025 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.039 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".