EpidEmiologic TrEnds in drug AbusE Proceedings of the Community Epidemiology Work Group Volume II
Bibliographic record
Abstract
of the Community Epidemiology Work Group (CEWG), who prepare the reports presented at the semiannual meetings, and representatives from other agencies who contribute data and technical knowledge. Appreciation is extended also to other participating researchers and Federal officials who contributed information. This publication was prepared by Social Solutions International, Inc., under contract number HHSN-2712007-000003C from the National Institute on Drug Abuse. This publication, Epidemiologic Trends in Drug Abuse, Volume II, contains the individual reports presented and data prepared for the June 2011 meeting by representatives from 20 areas in the United States. This publication also includes reports presented by researchers from Canada and New Zealand. Abstracts from local presenters in the Seattle area are also included. All material in this report is in the public domain and may be reproduced or copied without permission from the Institute or the authors. Citation of the source is appreciated. The U.S. Government does not endorse or favor any specific commercial product. Trade or proprietary names appearing in this publication are used only because they are considered essential in the context of the studies reported herein. For more information about the Community Epidemiology Work Group and other research-based publications and information on drug abuse and addiction, visit NIDA’s Web site at
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.019 |
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".