Desisting from Crime: In-Prison Behaviour and Cognition as Predictors of Post-Release Success
Bibliographic record
Abstract
To test the possibility that in-prison behaviour and cognition provide information useful in predicting future desistance from crime, two in-prison variables and nine pre-prison and demographic control variables were correlated with post-prison release success in a group of 1,101 male inmates released from federal prison. A Cox regression proportional hazards survival analysis revealed that fewer disciplinary infractions and lower criminal thinking predicted future desistance, as measured by the absence of post-release arrests or a longer time until first arrest for those who were arrested, net the effects of the pre-prison variables and demographic measures. When disciplinary infractions were subclassified as aggressive (fighting, assault, threatening) or non-aggressive (disobedience, theft, use of intoxicants), only the non-aggressive category achieved significance. Likewise, when criminal thinking was subdivided into proactive and reactive criminal thinking, only the reactive dimension achieved significance. These findings suggest that behaviour and cognition assessed in prison may have value both in predicting desistance upon a person’s release from prison and in clarifying the nature of post-prison release success.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".