Bibliographic record
Abstract
academics, and health practitioners committed to improving the health and safety of communities and individuals affected by illicit drugs. The network is comprised of leading experts from around the world who have come together in an effort to reduce drug-related harms by informing international drug policies with the best available scientific evidence. The primary objective of the ICSDP is to conduct and disseminate original scientific research, including systematic reviews and evidence-based drug policy guidelines. Through this work, the ICSDP seeks to meaningfully reduce drug-related harms by working collaboratively with communities, policy makers, law enforcement, and other stakeholders to help guide effective and evidence-based policy responses to the many problems associated with illicit drugs. The ICSDP’s research maintains the highest scientific standards through adherence to internationally accepted guidelines and protocols for systematic reviews and meta-analyses. Systematic reviews are conducted by designated working groups which include individual experts in systematic database searching (e.g., library science) and data synthesis (epidemiology) techniques and, where appropriate, seek to adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The ICSDP is a non-profit society registered in British Columbia, Canada. To learn more about the ICSDP and how your support can help improve the health and safety of communities and individuals affected by illicit drugs, please visit www.icsdp.org. International Centre
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 | 0.010 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.094 | 0.005 |
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".