Too risky to use, or too risky not to? Lessons learned from over 30 years of research on forensic risk assessment with Indigenous persons.
Bibliographic record
Abstract
= 237,729, non-Indigenous/White) and four documents identifying culturally relevant factors. Most measures demonstrated moderate predictive validity but often had significant ethnoracial differences, particularly for static measures. The Service Planning Instrument/Youth Assessment Screening Inventory, Level of Service Inventory youth variants, Psychopathy Checklist-Revised and Youth Version, and the Violence Risk Scale and its Sexual Offense version had the strongest predictive validity and least ethnoracial discrepancy. The Static Factors Assessment and Dynamic Factors Identification and Analysis-Revised had the weakest predictive validity. For Indigenous persons, the strongest individual predictors of recidivism were low education/employment, substance abuse, antisocial pattern, and poor community functioning, while mitigating factors that predicted decreased recidivism were measures of risk change (i.e., from culturally integrated programs combining mainstream and traditional healing approaches), cultural engagement/connectedness, and protective factors. In practice, static measures need to be supplemented with dynamic ones, and assessors should select measures with at least moderate predictive validity and ideally the least ethnoracial bias. These conclusions are tempered by the quantity and quality of the literature coupled with the circumstance that some study authors have coauthored tools in this review. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".