Bibliographic record
Abstract
needle sharing continues to fuel the HIV epidemic among IDUs in Canada. As elsewhere, subpopulations of IDUs have been identified as requiring special attention.1,2 One such population of IDUs is found in the Canadian prison system. In one study,3 38 % of female pris-oners and 26 % of male prisoners in a Quebec jail reported injecting drugs before they were incarcerated, 11 % of these women and 2 % of these males injected while in jail, and most of these reported sharing needles. Drug use has long been recognized as an unwanted factor in prison life and injec-tion drug use has been identified as a par-ticularly high-risk activity for prisoners.4,5 Recommendations to reduce risk of harm have included the distribution of needle and syringe cleaning kits and the imple-mentation of needle exchange programs.6,7 Corrections Services Canada (CSC) have responded to the problem of drugs in jails with education, limited bleach distribu-tion, and recently the addition of methadone maintenance therapy. The pri-mary response, however, has been focussed on interdiction. Testing prisoners for drugs has been a part of the CSC response since
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.006 |
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".