Assisted Desistance in Formal Settings: A Scoping Review
Bibliographic record
Abstract
Abstract Current research often relies on measures of recidivism to evaluate the effectiveness of formal criminal justice system interventions. Such studies, however, do not provide information on desistance from crime, that is, on how such interventions can help maintaining abstinence from offending and assist desisters in their efforts to change. This scoping review argues that formal agents (such as probation officers) can play a part in supporting desistance by providing practical help and resources based on desisters’ needs, and can assist in changes in self‐identity through sustained positive feedback and encouragement. We propose a model of assisted desistance to conceptualise the effects of formal agents on desistance processes. The mandatory context of interventions, the fragile balance between legalistic and therapeutic roles, as well as the processes of desistance outside of the criminal justice system are considered. Implications for future research, policy, and practice 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".