STRAIN, SOCIAL CAPITAL, AND ACCESS TO LUCRATIVE CRIME OPPORTUNITIES
Bibliographic record
Abstract
General strain theory (GST) posits that the experience of strains cause negative emotions that individuals try to alleviate through various strategies, including delinquency. GST predicts that the choice of delinquency as a coping solution will be more likely in certain conditions, including those where criminal opportunities are more abundant. The current study considers the role of strain as a direct predictor of lucrative criminal opportunities. Because we are specifically interested in lucrative, as opposed to routine criminal opportunities, our theoretical framework is also informed by research on criminal achievement which posits that offenders with more social capital are more likely to make money out of crime. Drawing from a sample of 170 juvenile offenders incarcerated in British Columbia, our results show that strain experiences are significantly associated with daily access to lucrative criminal opportunities, even after controlling for other factors, including negative emotions such as anger. Our results also indicate that criminal social capital – that is, the ability and willingness to collaborate with co-offenders in criminal pursuits – is strongly associated to access to lucrative criminal opportunities. The number of delinquent peers, however, did not emerge as a significant predictor. Theoretical and empirical implications for understanding and preventing juvenile delinquency 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".