First Person Accounts and Sociological Explanations of Delinquency*
Bibliographic record
Abstract
Cinquante‐six garçons du secondaire ont expliqué pourquoi ils avaient perpétré ou non certains actes de délinquance (combat, vandalisme, vol à l'étalage, usage de drogues). Ils ont aussi ciblé des théories sociologiques de la délinquance qui s'appliquent à leur comportement. Leurs réponses montrent que les deux types de données se recoupent beaucoup. L'effort, la théorie gánérate, les pairs, le contrôle social, les techniques de neutralisation et la prévention sont importants, mais non l'étiquetage ni l'imitation des médias. Une théorie de contingence de la délinquance est proposée. Fifty‐six high school boys were asked to explain in their own words why they had engaged in or refrained from certain delinquencies: fighting, vandalism, petty theft, truancy and drug use. They were also given the opportunity, via a checklist, to tell whether selected sociological theories applied to their behaviour. Their responses revealed considerable overlap in the two forms of data. Strain, general theory, peers, social control, techniques of neutralization and deterrence are important in varying combinations. Labelling and media imitation are not. A contingency theory of delinquency is proposed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".