Épisodes d’inactivité et revenus criminels dans une trajectoire de délinquance
Bibliographic record
Abstract
The instability of criminal activity over time is already well documented. However, little is known of the circumstances that can explain these short-term variations. It is possible that short-term transitions and changes precede turning points in criminal careers. For example, conditions that account for the temporary interruption of criminal activity might help explain a more definitive desistance from crime. Therefore, it appears appropriate to improve our knowledge of these factors. The study is based on the trajectories of 172 offenders involved in lucrative forms of crime. It focuses on changes in criminal earnings and episodes of temporary desistance within a window period of 36 months. The life history calendars method, combined with hierarchical models, is used for the analysis of the role of static (individual characteristics) and dynamic (life circumstances) factors in order to understand variations in criminal activity on a monthly basis. Results highlight the importance of events that mark offenders’ lifestyles and the parameters that characterize criminal involvement in predicting variations in observed trajectories. They also emphasize the importance of criminal achievement in explaining the decision of offenders to temporarily stop their illegal activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".