Implications for farmers of measures to reduce sugars consumption
Bibliographic record
Abstract
OBJECTIVE: To estimate the impact of reduced consumption of free sugars in line with World Health Organization recommendations, on sugar farmers globally. METHODS: Using multiregion input-output analysis, we estimated the proportional impact on production volumes of a 1% reduction in free sugars consumption by the public. We extracted data on sugar production from the Food and Agriculture Organization of the United Nations database for the top 15 sugar-cane- and beet-producing countries globally, and created a custom multiregion input-output database to assess the proportions of production going to human consumption, drawing on household expenditure surveys and national input-output databases (data valid for years 2000-2015). We also considered the impact on production volumes in relation to countries' gross domestic product. FINDINGS: A high proportion of current sugar production from these countries goes to human consumption, and would thus be impacted by reduced consumption of sugars. The largest impacts on cane sugar production, and thus on farmers, would likely occur in Brazil, China, India and Thailand and on beet production in Belarus, Germany, Russian Federation and United States of America. CONCLUSION: A global opportunity exists for public health leadership to bring together the health, economic, environmental and agriculture sectors to collaborate and build capacity for promotion of alternative livelihoods for sugar farmers. Lessons regarding strategy and the importance of political economy factors can be learnt from tobacco control measures. Further research to quantify the impact of reductions in sugars consumption would provide useful insights for designing policies to complement and strengthen efforts to improve diets and health.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".