The Effect of Prosocial and Antisocial Relationships Structure on Offenders’ Optimism towards Desistance
Bibliographic record
Abstract
At the end of his career, Carlo Morselli started to be interested in how the structure of social relations could influence offenders’ prospects for reintegration and desistance. This article analyzes the data from his research project on that topic. The impacts of offenders’ relationships have traditionally been discussed from a dichotomous, risk-centered perspective opposing antisocial and prosocial peers. Social network studies allow a step back and a global view of the contexts and processes in which relationships shape trajectories. This article focuses on the ego networks of offenders as they reintegrate with society and sheds light on triadic patterns associated with increased optimism toward desistance. Interviews were conducted with residents of halfway houses (48 men and 24 women), with offenders followed by a community agency (25 men), and with incarcerated youth offenders (24 male teenagers). Structured interviews addressed multiple aspects of the lives of the offenders, including their social relations, prosocial and antisocial. A mixed-method approach was used to understand the influence of social relations in the perception of desistance potential success. First, logistic regressions were used to assess the effect of individual’s and egocentric networks’ characteristics on optimism toward desistance. Second, case studies of ego network sociograms illustrate the results and suggest hypotheses about processes that may explain them. Results show that optimism is higher when prosocial personal networks are denser, and is lower when antisocial networks are open, and as antisocial peers are connected to prosocial ties. The implications of these patterns for offenders’ desistance and network-based interventions are discussed.
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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 | 0.005 |
| 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) | 0.001 | 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".