Offense Specialization among Serious Habitual Juvenile Offenders in a Canadian City during the Early Stages of Criminal Careers
Bibliographic record
Abstract
Given that social change has been historically associated with differentiation, we investigate this process within unconventional careers. Our study questions whether criminal careers may be the manifestation of time-invariant factors or whether they may vary as a result of social factors and circumstances at different transitional points. Using a sample of official police data (N = 191), this study examines offense specialization among serious habitual juvenile offenders as indicators of behavioral turning points in the onset and desistance of criminal careers. Using Farrington, Snyder, and Finnegan's Forward Specialization Coefficient analysis (1988), our findings suggest a reconsideration of the development of both stability and change, for different offenses at different age-related transitional stages in delinquent careers. Our analysis suggests that delinquency may be organizationally similartoconventional occupational formsofspecialization, differentiation, and social capital.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".