Alcohol control policies in low- and middle-income countries: Testing impacts and improving policymaking practice
Bibliographic record
Abstract
Room, R. (2014). Alcohol control policies in low- and middle-income countries: Testing impacts and improving policymaking practice. The International Journal Of Alcohol And Drug Research, 3(3), 184 – 186. doi:http://dx.doi.org/10.7895/ijadr.v3i3.181Alcohol is a major contributor to the global burden of disease (Lim et al., 2012), and is a major source of health and social harm in many middle- and low-income countries, as well as in high-income countries. In recognition of this, a Global Strategy to Reduce the Harmful Effects of Alcohol was adopted in 2010 by the World Health Organization’s governing body, the World Health Assembly (WHA) (WHO, 2010). Since then, there has also been increasing international recognition of alcohol’s role in social problems, including crime, family problems, and lost work productivity: "beyond health consequences," WHO notes, "the harmful use of alcohol brings significant social and economic losses to individuals and society at large" (http://www.who.int/mediacentre/ factsheets/fs349/en/). New emphasis has been put, too, on alcohol’s major contribution as a risk factor for non-communicable diseases (NCDs) such as cancer, heart disease, and liver cirrhosis; WHO’s global goals for NCD control include the (somewhat fuzzily defined) goal of a 10% reduction in the "harmful use of alcohol . . . as appropriate" by 2020 (WHO, 2013). Together, these steps reflect a greater international recognition of alcohol as a major issue to be addressed in improving global health
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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 | 0.214 | 0.287 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".