Estimating the Flow of Methamphetamine and Other Synthetic Drugs from Quebec, Canada, 1999-2009
Bibliographic record
Abstract
These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. In this study, researchers used capture-recapture sampling and multiple data sources to gauge the impact of drug trafficking in Quebec, Canada on the United States drug market. The main analyses were based on arrest data that were obtained for Quebec. In addition, analysis of the chemical composition and price assessments of the Quebec synthetic drugs was done. The study includes one SPSS data file (Quebec Arrest Data (Synthetic Drugs Cases, September 2014; n=20261)-ICPSR.sav ; n=20,261 ; 13 variables) and one Excel data file (Chemical composition of seized synthetic drugs.xls ; n=365 ; 14 variables). Spatial analyses of border seizure data was performed by the researchers, but these data are not available at this time. The data used for these analyses concerned synthetic drug seizures at Canadian borders from 2007 to 2012. The dataset was provided by the Canadian Border Services Agency (CBSA). For each seizure, the specific border crossing where the seizure was made was provided, as well as the value of the seizure (except for precursors), the country of origin and the type of drug seized. The types of drugs were classified into five types: (1) Precursors, (2) MDMA, (3) Amphetamine, (4) Methamphetamine and (5) Others. Most of the seizures (86.6 percent) were classified in this last category. The country of origin of the seizure was also provided.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".