Ecstasy, legal highs and designer drug use: A Canadian perspective
Bibliographic record
Abstract
Recreational drug use in Canada is not uncommon, but as with most societies, illegal drug use carries harsh penalties resulting in a criminal record when an individual is successfully prosecuted. Popular drugs of use in Canada include ecstasy, cannabis (including some synthetic cannabinoids sold as ‘Spice’ and ‘Incense’) and several emerging psychoactive ‘legal highs’. Surprisingly, Canada is a major manufacturer and exporter of the popular club drug ecstasy, with criminal gangs organising the synthesis and distribution of this club drug worldwide. Over the last 18 months, there has been much interest in and use of alternatives to ecstasy due to contamination of ecstasy during synthesis. One particular contaminant, paramethoxymethamphetamine (PMMA), has resulted in several deaths. Other alternatives include piperazines and mephedrone analogues. With regard to cannabis, some is home grown within people’s properties, but there is also large-scale cultivation in British Columbia where the climate is more temperate. With the introduction of corporate drug screening, there is increasing use of synthetic cannabinoids to avoid detection of marijuana use. This article discusses the problems and trends of synthetic drug use in Canada and reflects on the limited education available to youth in this regard.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".