Disentangling Promotive and Buffering Protection: Exploring the Interface Between Risk and Protective Factors in Recidivism of Adult Convicted Males
Bibliographic record
Abstract
The quality of risk assessment instruments has improved greatly during the last 40 years. While assessing protective factors has become common practice, with some instruments now devoted entirely to such assessments, little is known about the effect of risk and protective factors on recidivism. The present study investigates the effects (promotive or buffering protective) of protective factors captured by the LS/CMI for a sample of 18,031 convicted adult males under the supervision of provincial services in Canada. Effects of protective factors and possible interactions between risk and protective factors were investigated using moderation analyses. Results indicate that protective factors can be both promotive and buffering protective for risk and that the benefits of protective factors are related to the risk to which people are exposed. Patterns of protective effects appear to differ for general and violent recidivism. Theoretical and clinical implications are discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".