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