Bibliographic record
Abstract
Jiang, H., & Room, R. (2016). The many facets of alcohol policy. The International Journal Of Alcohol And Drug Research, 5(3), 85-87. doi:http://dx.doi.org/10.7895/ijadr.v5i3.234The articles in this section are revised from papers presented at a thematic meeting on alcohol policy research of the Kettil Bruun Society for Social and Epidemiological Research on Alcohol, held in Melbourne in September, 2014. The international meeting was titled “Alcohol Policy Research: Putting Together a Global Evidence Base,” with attendees from 15 countries across five continents, including researchers from Australia, Europe, North America, Africa, and Asia. Papers revised from presentations at the conference are also published as special issues or sections in three other journals: Alcohol and Alcoholism (in Volume 50, No. 6), Drug and Alcohol Review (in Volume 35, No. 1), and Contemporary Drug Problems (in Volume 42, No. 2).
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".