Psychometric Properties of a Risk Tool Across Indigenous Māori and European Samples in Aotearoa New Zealand: Measurement Invariance, Discrimination, and Calibration for Predicting Criminal Recidivism
Bibliographic record
Abstract
Due to recent legal cases highlighting a lack of cross-ethnicity validity research using correctional risk assessment tools, we evaluated psychometric properties of Dynamic Risk Assessment for Offender Re-entry (DRAOR) scores across Māori ( n = 1,812) and New Zealand (NZ) European samples ( n = 1,211) in Aotearoa NZ. Using routine administrative data, our analyses suggested scoring properties were invariant across ethnicity for 15 of 19 items. Discrimination properties were also equivalent, but we observed a higher recidivism base rate among Māori participants, consistent with official statistics. Consequently, calibration analyses using a fixed follow-up ( N = 372) demonstrated higher predicted recidivism rates for Māori participants at each DRAOR score. This suggests that Māori participants with similar levels of DRAOR-assessed need factors as NZ European participants experienced relatively greater continued justice contact. DRAOR users should prioritize delivering quality case management to clients, recognizing that both case-specific and systemic factors may underlie differential base rates.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".