Examining dynamic risk and strength profiles for Indigenous and non-Indigenous young adults
Bibliographic record
Abstract
Young adults are particularly at risk for involvement in the justice system relative to older adults. Little research specifically examines age-related differences in salience of dynamic risk and strength factor scores on recidivism (e.g. Spruit, A., van der Put, C., Gubbels, J., & Bindels, A. (2017). Age differences in the severity, impact and relative importance of dynamic risk factors for recidivism. Journal of Criminal Justice, 50, 69–77) and no research specifically examines this question in Indigenous Canadian adults. To address this gap, the current study examines the predictive accuracy and calibration of a risk-needs assessment tool, the Service Planning Instrument (SPIn), by age group and examines age-related differences in prevalence and salience of dynamic risk and strength scores on recidivism for both Indigenous and non-Indigenous male adults. The authors obtained SPIn assessment data completed over a 6-year period and recidivism data with a fixed 3-year follow-up for justice-involved male adults on community supervision in a single province in Canada (N = 16,596). Risk and strength profiles for Indigenous and non-Indigenous young adults were relatively similar. Age moderated the relationship between several dynamic risk and strength factor scores and recidivism for non-Indigenous individuals; no factors were moderated by age for Indigenous individuals.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".