Alcohol industry, corporate social responsibility and country features in Latin America
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Research on corporate behaviour can contribute to the understanding of the possible adverse impacts of alcohol-industry corporate social responsibility (CSR) initiatives and their potential influence on policymaking. This study explores the association between alcohol-industry CSR activities and selected country features in Latin America and the Caribbean. DESIGN AND METHODS: Nine health experts evaluated 148 CSR activities using a standardised protocol; activities were classified into the categories risk management CSR (rmCSR), that is, to avoid/rectify externalities (n = 67), and strategic CSR, that is, to fulfill philanthropic responsibilities (n = 81). We evaluated the associations, separately, between the number of rmCSR and of strategic CSR actions in each country with threats from public health measures (specifically, the level of research into alcohol consumption and harms, the existence of an alcohol surveillance system and the number of governmental alcohol policy actions) and per capita alcohol consumption; we adjusted by economic indices (country income level and the gross domestic product) and population size. RESULTS: Multivariate analyses showed that the higher the level of alcohol research within a country and its per capita consumption, the more likely rmSCR activities were to occur, independently of the country's economic development or population. DISCUSSION AND CONCLUSIONS: Results suggest rmSCR actions could be implemented as a way to preserve markets by counteracting scientific evidence about alcohol related harms. This evidence could serve as a starting point to future research, contributing to the understanding of alcohol industry behaviour and the advancement of effective public policies.
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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 | 0.000 | 0.000 |
| 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 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".