The Road to Desistance: The Relationship between Formal Institutions of Social Control, Informal Social Bonds, and Intermittency in Offending
Bibliographic record
Abstract
The current study examines the relationship between formal social control, informal social bonds, and intermittency in offending. Using data from a Canadian population who have been formally flagged as representing a disproportionate risk to public safety because of their frequent and serious offending over the life course, (N = 376), we use multiple-failure survival analysis to examine whether formal social control (probation supervision) and informal social bonds (to employment, family, and other prosocial community supports) are related to intermittency in offending. We also investigate whether social bonds moderate the relationship between probation and intermittency. Findings indicate that probation, employment and romantic relationships reduce the hazard of reoffending. Longer periods of probation were related to a lower hazard of reoffending for those who have been employed for longer than six months. While probation length has an effect on intermittency, the mere presence of social bonds reduces the hazard of reconviction. Given the importance of prosocial relationships outside of formal systems of control, we argue for policies that are non-punitive, particularly since lengthy periods of probation can also extend system involvement, which can stall the desistance process.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".