Tactics and practices of the alcohol industry in Latin America: What can policy makers do?
Bibliographic record
Abstract
Zhang, C. & Monteiro, M. (2013). Tactics and practices of the alcohol industry in Latin America: What can policy makers do? International Journal of Alcohol and Drug Research, 2(2), 75-81-6. doi: 10.7895/ijadr.v2i2.95 (http://dx.doi.org/10.7895/ijadr.v2i2.95)Aim: This paper describes the practices and tactics of the alcohol industry in Latin America, focusing on industry globalization and consolidation, implementation of research studies, marketing, and corporate responsibility initiatives, and discusses how these areas of influence may have an impact on alcohol policy development in this region.Design: The information provided here is drawn from an international literature review, news websites, and informal communications with officials in Ministries of Health and researchers from Latin America, and from annual reports and websites sponsored or maintained by major alcohol companies operating in Latin America.Setting: Latin America and Caribbean RegionFindings: Industry globalization and consolidation, implementation of research studies, marketing, and corporate responsibility initiatives are major activities of the alcohol industry in Latin America that can influence alcohol policy making.Conclusions: We conclude that implementing effective alcohol control policies is likely to fail if the influence and actions of the alcohol industry cannot be managed by policy makers in countries of this region. There is a need to increase knowledge of the alcohol industry’s role and actions and of its conflicts of interest with public health, and to build capacity across various sectors of government to implement effective policies, using clear rules for engagement. Research on alcohol marketing, corporate social responsibility practices and the industry’s influence on policy making should be a priority in emerging markets in general, but particularly in Latin America and the Caribbean.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".