Introduction to a special issue on alcohol control policies in low and middle income countries
Bibliographic record
Abstract
Sornpaisarn, B., & Shield, K. (2014). Introduction to a special issue on alcohol control policies in low and middle income countries. The International Journal Of Alcohol And Drug Research, 3(3), 182 – 183. doi:http://dx.doi.org/10.7895/ijadr.v3i3.182In response to the World Health Assembly’s adoption in 2010 of a resolution which endorsed a Global Strategy to Reduce the Harmful Use of Alcohol, many countries, especially those considered low- and middle-income (LMIC), formulated, and in some instances implemented, a variety of alcohol control policies. However, the supporting knowledge and evidence used to evaluate the effectiveness of alcohol control policies stem primarily from high-income countries (HIC) (Babor et al., 2010; World Health Organization, 2010). This lack of knowledge and evidence from LMIC is a considerable public health problem, as differences between the socio-economic and cultural contexts of LMIC and HIC may influence the effectiveness of an alcohol control policy (Anderson et al., 2009; Lachenmeier, 2011).
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.058 | 0.022 |
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".