Attributable fractions for alcohol and illicit drugs in relation to crime in Canada: conceptualization, methods and internal consistency of estimates
Bibliographic record
Abstract
A research programme in Canada is aimed at estimating attributable fractions for the use of alcohol and illicit drugs in relation to crime. Analyses from two studies of new inmates in federal penitentiaries are presented, the first based on a computer-driven questionnaire completed by 8,598 inmates and the second on interviews with 477 inmates. One method used in the estimation combined the following three models linking psychoactive substances to criminal behaviour: the intoxication model, the economic model and the systemic model. Data pertaining to the first two models were used to illustrate this method. Consistency checks showed that crime events attributed to illicit drugs or alcohol were concordant with the inmate being addicted to a substance, and with the inmates' overall assessments of drugs or alcohol on their criminality. Issues discussed include validity, the extent to which findings can be generalized and the advantages and drawbacks of basing attributable fraction estimates on data from self-reports on individual crime events.
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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 | 0.046 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 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".