Demon Rum: High-Tech Solutions to an Age-Old Problem
Bibliographic record
Abstract
This article summarizes the proceedings of a symposium at the 2004 Research Society on Alcoholism Meeting in Vancouver, British Columbia, Canada, organized and chaired by Scott T. Walters. The purpose of the symposium was to describe several brief motivational interventions offered via the Internet, including the evidence for web-based interventions, applications and contexts in which such approaches are being used, and directions for future research. Walters provided an overview and introduction to the topic and discussed the e-CHUG (www.e-chug.com) and e-TOKE (www.e-toke.com) feedback interventions for college alcohol and marijuana prevention, including the contexts in which they are being used and ways they are being integrated with other campus prevention efforts. Dr. Hester presented 12-month results from a controlled trial of the Drinker's Check-up (www.drinkerscheckup.com), an intervention for adult problem drinkers that is available both as a Windows and as an Internet application. Dr. Chiauzzi described the development and testing of My Student Body (www.mystudentbody.com), a tailored drinking prevention web site for college students. Finally, Dr. Miller addressed the use of online assessment and feedback to reduce drinking, including the history of web-based interventions and their likely future and the potential limitations of such approaches.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".