Classifying lifestyles: a re-examination of the drug-crime nexus among Toronto high-school youth
Bibliographic record
Abstract
An extensive body of research reveals the strong relationship between psychoactive substance use, delinquency, and criminality. The drug-crime relationship is constructed in a number of ways, as direct, reciprocal, spurious, and independent. Much of the existing research examines the direct relationship between substance use and criminality, and focuses upon specific events in which these two behaviours interact. Less well known is the nature of the drug-crime relationship as part of broad, deviant lifestyle. This thesis adopts the recent work of Gottfredson and Hirschi (1990) in order to re-examine the drug-crime nexus as part of a deviant lifestyle. Analyses are based on data from the Toronto Youth Crime & Victimization Survey (TYCVS). The TYCVS is a random survey of 3,393 adolescents attending 31 high schools across Toronto. The objectives of this thesis are: (1) to test whether the relationship between psychoactive substance use, delinquency, and criminality is spurious; (2) to assess whether adolescent exhibit specialization or versatility in their psychoactive substance use, and how this shapes specialization or versatility in non-drug offending; and (3) to identify a typology that helps to classify adolescents by their patterns of psychoactive substance use, delinquency, and criminality. Throughout, gender differences in the drug-crime relationship are explored. Findings provide support for the examination of the drug-crime nexus as a lifestyle. First, the relationship between substance use and criminality is not spurious. Rather, the social processes that explain involvement with drugs are unique from those that account for criminality. Second, adolescents tend to exhibit both specialization and versatility in their substance use. This in turn affects patterns of involvement in non-drug offending. Specialization in substance use is correlated with specialization in non-drug offending, while versatility in substance use is clearly linked to offending versatility. Third, distinct clusters of adolescents are differentiated by their substance use and criminality, as well as the frequency and seriousness of their involvement. These findings suggest that we cannot treat the drug-crime relationship homogeneously; rather, the myriad of groupings by which substance use and criminality interconnect challenge assumptions that most drug use is linked to crime. Theoretical implications of these findings 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".