Is the alcohol industry doing well by ‘doing good’? Findings from a content analysis of the alcohol industry’s actions to reduce harmful drinking
Bibliographic record
Abstract
OBJECTIVES: The aims of this study were to: (1) describe alcohol industry corporate social responsibility (CSR) actions conducted across six global geographic regions; (2) identify the benefits accruing to the industry ('doing well'); and (3) estimate the public health impact of the actions ('doing good'). SETTING: Actions from six global geographic regions. PARTICIPANTS: A web-based compendium of 3551 industry actions, representing the efforts of the alcohol industry to reduce harmful alcohol use, was issued in 2012. The compendium consisted of short descriptions of each action, plus other information about the sponsorship, content and evaluation of the activities. Public health professionals (n=19) rated a sample (n=1046) of the actions using a reliable content rating procedure. OUTCOME MEASURES: WHO Global strategy target area, estimated population reach, risk of harm, advertising potential, policy impact potential and other aspects of the activity. RESULTS: The industry actions were conducted disproportionately in regions with high-income countries (Europe and North America), with lower proportions in Latin America, Africa and Asia. Only 27% conformed to recommended WHO target areas for global action to reduce the harmful use of alcohol. The overwhelming majority (96.8%) of industry actions lacked scientific support (p<0.01) and 11.0% had the potential for doing harm. The benefits accruing to the industry ('doing well') included brand marketing and the use of CSR to manage risk and achieve strategic goals. CONCLUSION: Alcohol industry CSR activities are unlikely to reduce harmful alcohol use but they do provide commercial strategic advantage while at the same time appearing to have a public health purpose.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".