ILLICIT DRUGS AND CRIME IN CANADA
Bibliographic record
Abstract
n Although the overall rate of police-reported drug offences has increased 12 % since 1993, the long-term trend has generally remained stable over the past 15 years. It must be noted that trends in drug offences are directly influenced by levels of police enforcement. n After a ten-year decline, the rate of cannabis offences has increased by 34 % since 1991. Conversely, the rate of cocaine offences increased between 1981 and 1989, but has dropped by 36 % since 1989. The rate of heroin offences also increased for a number of years, peaking in 1993, and then falling 25 % over the last four years. n Cannabis-supply offences (trafficking, importing and cultivation) increased for the fourth consecutive year in 1997, partially driven by an increase in cultivation offences. Cannabis-possession offences increased steadily from 1991 to 1996, but dropped slightly in 1997. n Cannabis offences continue to account for the majority of all drug offences. More than 7 in 10 drug offences reported in 1997 involved cannabis. Two-thirds of cannabis offences were for simple possession. n British Columbia continued to show the highest rate (426 offences per 100,000 population) of drug offences in 1997, almost twice the national average. However, when examining only the number of persons charged with drug offences, the rate for British Columbia was only 41 % greater than the national average. Newfoundland reported the lowest rate (132) of drug offences for the second year in a row.
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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 | 0.005 | 0.007 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".