Favoring Crime Desistance and Social (Re)Integration of Offenders Through Intersectoral Partnerships
Bibliographic record
Abstract
The purpose of this chapter is to show the importance of intersectorality in partnerships to successfully understand and influence the processes of crime desistance and of social and community (re)integration of people subject to judicial control. It begins with an outline of the “what works” and “how it works” movements and provides tools to help understand such notions as crime desistance, (re)integration, trajectories, and intersectorality. After describing the objectives of the (RÉ)SO 16-35 partnered research project, the authors present various intersectoral collaborative initiatives in the United Kingdom, the United States, and Canada and indicate what, according to the literature, contributed to their development. The chapter concludes with the identification of two central principles in the development of intersectoral partnerships aiming to favor crime desistance and social and community (re)integration trajectories: a culture of dialogue must be instilled, and the initial objective of the project must be kept in mind.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".