CRIMINAL ACHIEVEMENT, OFFENDER NETWORKS AND THE BENEFITS OF LOW SELF‐CONTROL
Bibliographic record
Abstract
This study follows recent research on criminal earnings and examines the impact of underlying traits (low self‐control) and personal organization features (nonredundant networking) on the criminal earnings of a sample of incarcerated offenders previously involved in market and predatory crimes. Controlling for various background factors (age, noncriminal income, lambda and costs of doing crime), both low self‐control and nonredundant networking independently explain why some offenders are more successful than others in achieving higher monetary standards through crime. Although efficient, brokerage‐like networking enhances market offenders' earnings, low self‐control emerges as an asset for predatory offenders: the lower their self‐control, the higher their criminal earnings. For market offenders, however, low self‐control has no direct effect, but it does mitigate the impact of effective networking on criminal earnings. The results emerging from this study have implications for Gottfredson and Hirschi's theory of crime and the advent of a criminal network perspective. Extensions are also made toward the conventional/criminal embeddedness framework and deterrence research.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".