Bibliographic record
Abstract
This issue of the Journal features the publication of one of the more controversial studies regarding that most contested of human immunodeficiency virus (HTV) prevention interventions, needle exchange pro-grams (NEPs) (1). The Montreal study, long a focus of speculation by researchers and of misrepresentation by the opponents of NEPs, even as it had remained un-published, will now rise or fall on its own merits. In my view, the study raises no substantial concerns about the effectiveness of NEPs but does point the way to improving the programs. Some historical background is essential if the place of the present study in the body of needle exchange research is to be fully appreciated. In the fall of 1993, the US Centers for Disease Control and Prevention completed a review of NEP effectiveness and con-cluded that NEPs are likely to reduce the incidence of HIV among injection drug users (IDUs) and do not appear to be associated with an increase in drug use rates, the two criteria that must be met for the US federal ban on NEP funding to be lifted. Realizing the political implications of a document in which senior federal scientists recommended that the federal fund-ing ban be lifted, a course seemingly at odds with the government "zero tolerance " policy on drugs, the ad-ministration chose to suppress the troublesome docu-ment (2). This strategy stifled the debate until the Washington Post obtained the documents in February 1995. This is where the Montreal study became a pawn in the US debate over needle exchange. Forced to justify how the government had ignored its own scientists ' recommendations, Assistant Secretary for Health Philip R. Lee defended the ban by citing three unpublished studies, including the Montreal study, Received for publication March 20,1997, and accepted for pub-
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.856 | 0.749 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".