Friends with(out) benefits: co-offending and re-arrest
Bibliographic record
Abstract
Research shows that co-offending has contradictory effects on rates of re-arrest. On the one hand, group offending may be riskier: for example, co-offenders might be targeted by police or might snitch to protect themselves. Criminal networks may also have indirect effects: offenders embedded in criminal networks commit more offences and thus should have a higher risk of being arrested at some point. On the other hand, networks generate steady criminal opportunities with relatively low risk of arrest and high monetary benefits (e.g. drug trafficking). Few authors have empirically explored the relation between co-offending and re-arrest. This article does so using data from seven years of arrest records in the province of Quebec, Canada. The analysis is designed to explore why some offenders are re-arrested after an initial arrest while others are not. It focuses on the factors involved in re-arrest, considering two distinct levels of measures of co-offending. The first level of analysis takes into account a situational measure that indicates whether a given offence was committed by co-offenders (group offence). The second level is used to examine whether being part of a criminal network influences re-arrest. For offenders embedded in such networks, two network features (degree centrality and clustering coefficient) show that the global position of individuals within the Quebec arrest network are analysed. Our results suggest that co-offending is a crucial factor that should be taken into account when looking at the odds of being caught again. The use of generalised linear mixed model brings interesting nuances about the impact of co-offending. The article adds to the recently growing literature on the link between networks and criminal careers.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".