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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".