Evaluating Alcohol Industry Action to Reduce the Harmful Use of Alcohol
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For the year 2013, it is estimated that alcohol was the world's sixth leading risk factor for disability-adjusted life years (DALYs), after high blood pressure, smoking, high body mass index, childhood undernutrition and high fasting plasma glucose (GBD 2013 Risk Factors Collaborators, 2015). The numbers of age-standardized alcohol-attributable deaths and DALYs were 11.1 and 13.6% higher in 2013 than in 1990, respectively. Reduction in alcohol consumption is essential to achieve global targets of reducing deaths from non-communicable diseases by 25% between 2010 and 2025 (Kontis et al. , 2014), and WHO has set a target of reducing the harmful use of alcohol by 10% between 2010 and 2025 (WHO, 2014a,b), largely operationalized by measuring levels of per capita adult alcohol consumption. The evidence base for effective measures to reduce alcohol consumption is robust (see Anderson et al. , 2009, 2012, 2013). It has been pointed out that improvements in alcohol-related health cannot be done by ministries of health alone, but require whole of government and whole of society approaches (see World Health Organization (WHO) publications: Kickbusch and Gleicher, 2012; Kickbusch and Behrendt, 2013), including action by the alcohol industry (OECD, 2015). Indeed, WHO's Global Strategy to reduce the harmful use of alcohol (WHO, 2010) states: ‘(p. 20) Economic operators in alcohol production and trade are important players in their role as developers, producers, distributors, marketers and sellers of alcoholic beverages. They are especially encouraged to consider effective ways to prevent and reduce harmful use of alcohol within their core roles mentioned above, including self-regulatory actions and initiatives.’ On 9 December 2015, AB InBev, the world's largest producer of beer, and soon likely to become even larger (Collin et al. , 2015), launched its four ‘drinking goals’ 2015–2025, aiming to reduce the harmful use of …
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it