Bibliographic record
Abstract
Global Strategy to Reduce the Harmful Use of Alcohol: First Step towards an Alcohol Framework Convention? English Summary: At the 61st World Health Assembly, the 193 member states discussed and ratified the global strategy to reduce the harmful use of alcohol. Firstly, 10 target areas have been identified within the strategy and alcohol policy should be structured according to these areas. The contribution of Anderson (2011) discusses these target areas with respect to supporting empirical evidence and policy implications. The final target area is Monitoring and Surveillance, and the other two contributions fall under this topic. Shield, Rehm, Patra & Rehm (2011) provide an overview of worldwide adult per capita consumption. Per capita consumption is associated indirectly to alcohol-related harm: as higher consumption generally leads to more harm, but the level of association varies according to economic indicators. Countries with lower GDP PPP experience more harm as they have more risks associated to alcohol such as infectious diseases like tuberculosis and/or a less developed health care system. The last contribution to this topic focuses solely on Germany and includes both health as well as social consequences ( Kraus, Piontek, Pabst & Bühringer, 2011 ). It needs to be recognized that the global strategy contains merely suggestions that are not binding to any of the member states. It is not yet shown whether this strategy is enough to combat the rising global alcohol-related harm and there have been suggestions to adopt a more binding form of international arrangement such as the Framework Convention for Tobacco Control.
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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".