Globalization, diet, and health: an example from Tonga.
Bibliographic record
Abstract
The increased flow of goods, people, and ideas associated with globalization have contributed to an increase in noncommunicable diseases in much of the world. One response has been to encourage lifestyle changes with educational programmes, thus controlling the lifestyle-related disease. Key assumptions with this approach are that people's food preferences are linked to their consumption patterns, and that consumption patterns can be transformed through educational initiatives. To investigate these assumptions, and policies that derive from it, we undertook a broad-based survey of food-related issues in the Kingdom of Tonga using a questionnaire. Data on the relationships between food preferences, perception of nutritional value, and frequency of consumption were gathered for both traditional and imported foods. The results show that the consumption of health-compromising imported foods was unrelated either to food preferences or to perceptions of nutritional value, and suggests that diet-related diseases may not be amenable to interventions based on education campaigns. Given recent initiatives towards trade liberalization and the creation of the World Trade Organization, tariffs or import bans may not serve as alternative measures to control consumption. This presents significant challenges to health policy-makers serving economically marginal populations and suggests that some population health concerns cannot be adequately addressed without awareness of the effects of global trade.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".