Canadian Addiction Survey, 2004
Bibliographic record
Abstract
The Canadian Addiction Survey (CAS) is a collaborative initiative sponsored by Health Canada, the Canadian Executive Council on Addictions(CECA) - which includes the Canadian Centre on Substance Abuse (CCSA), the Alberta Alcohol and Drug Abuse Commission (AADAC), the Addictions Foundation of Manitoba (AFM), the Centre for Addiction and Mental Health (CAMH), the Prince Edward Island Provincial Health Services Authority, and the Kaiser Foundation - the Centre for Addictions Research of BC (CAR-BC), and the provinces of Nova Scotia, New Brunswick and British Columbia. The key objectives of the proposed CAS are as follows: To determine the prevalence, incidence and frequency of alcohol and other drug use in the Canadian population aged 15 and older. The drugs of interest include alcohol, tobacco, illicit drugs, including cannabis, heroin and other opiates, cocaine and crack, amphetamines, hallucinogens (including MDMA) and inhalants. To assess the context of use and the extent of harms that result from those individuals who use drugs. Measures include indicators of hazardous and harmful drinking, dependence and abuse indicators, and the adverse effects on personal and social functioning. To identify the risk and protective factors related to the use and consequences of drug use in the general population and in specific sub-groups. To assess the public’s opinions, views and knowledge regarding existing andpotential addiction policies and to identify emerging policy issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.015 |
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".