Reports on drug supply in the americans 2022
Bibliographic record
Abstract
The "Report on Drug Supply in the Americas 2022" was orchestrated by the Inter-American Drug Abuse Control Commission (CICAD) within the Organization of American States (OAS). The report offers insights on drug supply trends in the Americas, drawing data from 33 OAS member states including nations like Brazil, Canada, Mexico, and the United States. These nations collaborated through the Technical Working Group on Drug Supply Indicators or enabled data access from local agencies, supported by additional qualitative interviews. Key contributors included Tiffany Barry, Daniela Chiriboga, Dr. Carolina Sampo, and Dr. Bryce Pardo. The report aligns with the OAS Hemispheric Drug Strategy 2020, revealing the complexities of the drug problem in the region and emphasizing evidence-based policies. The overarching aim is to provide a comprehensive view of the drug landscape in the Americas, aiding in the formulation of data-driven drug policies. The alarming evolution of the drug trafficking problem, including heightened drug production and potency and the complexities introduced by technology and transnational criminal organizations, underscores the importance of this collaborative report.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".