Local Drug Use Epidemiology: Lessons Learned and Implications for Broader Comparisons
Bibliographic record
Abstract
The Canadian Community Epidemiology Network on Drug Use (CCENDU) Vancouver-site committee is comprised of representatives from national/provincial/local health and enforcement agencies. It collects, collates and interprets recent local data relating to major drug use to produce regular reports exploring Vancouver data with provincial and national comparisons. Meetings of committee members allow identification of current concerns, inform the data and broaden the context for the members. The seventh Vancouver site report since 1996 was published in July 2007. Data trends are explored with input from committee members; changes in data collection and definitions are clarified. The committee strives to share the knowledge with health authorities, policy makers, agencies and the public.
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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 | 0.077 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".