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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".