Alcohol labelling rules in free trade agreements: Advancing the industry's interests at the expense of the public's health
Bibliographic record
Abstract
Introduction and Aims: The Trans-Pacific Partnership Agreement (TPP) included novel rules for wine and spirits requiring parties to allow wine and spirits importers to display information required by the importing country on a supplementary label rather than on the standard label. Since the TPP negotiations concluded, alcohol-specific supplementary labelling rules have begun to appear in other trade agreements. The aim of this paper was to map the new instruments containing these rules and examine developments in the rules with implications for health information on alcohol containers. Design and Methods: Trade agreements signed after the TPP negotiations concluded were retrieved and searched for alcohol-specific labelling provisions. A legal analysis of these provisions and related exceptions was undertaken. Results: Supplementary labelling rules similar or identical to those in the TPP have been included in five subsequent trade agreements. The United States–Mexico–Canada Agreement also includes several additional provisions about alcohol labelling. Exceptions in the agreements provide some space for governments to defend labelling measure that might otherwise breach the rules, in the event of a dispute. Discussion and Conclusions: By securing these rules, the alcohol industry is better positioned to claim the space on the standard label as industry ‘real estate’ and to oppose mandatory health information incorporated into the standard labelling. These risks can be mitigated by stemming the adoption of supplementary labelling rules in further trade agreements; clarifying the text of agreements and ensuring that regulators understand that the rules do not prevent the use of ‘best-practice’ warning labels.
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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 | 0.034 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".