A Second Proof of Concept Investigation of Strengths Using the Structured Assessment of Violence Risk in Youth Tool With Justice-Involved Youth: Item Level Risk-Based Effects and Interactions
Bibliographic record
Abstract
Despite efforts to incorporate protective factors or 'strengths' in applied risk assessments for criminal reoffending, there has been limited progress towards a consensus regarding what is meant by such terms, what effects predictors can exert, or how to describe such effects. This proof of concept study was undertaken to address those issues. A structured professional judgment tool was used to create lower and higher historical/static risk groups with a sample of 273 justice-involved male youth with sexual offenses followed over a fixed 3-year period. Using risk and protective poles to create pairs of dichotomous variables from trichotomously rated risk and protective items, risk-based exacerbation and risk-based protective effects were found. These varied in terms of whether the effect on the outcome of a new violent (including sexual) offense was larger, smaller, or absent for youth at higher or lower historical/static risk. Some of these potentially dynamic dichotomous variables were shown to have a protective (or risk) effect after controlling for both historical/static risk and that same item's risk (or protective) effect. Some moderated the association between historical/static risk and recidivism, strengthening or reducing it. Terms for these effects and implications of incorporating strengths in research and applied practice were considered.
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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 | 0.051 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".