Globalization, Health and the Nutrition Transition: How Global TNCs are Changing Local Food Consumption Patterns
Bibliographic record
Abstract
Food consumption patterns around the world are changing. In general, individuals around the globe are consuming more edible oils and sugars than they were twenty years ago. What has lead to this nutrition transition? Scholars have identified a range of mechanisms associated with the transition, but nearly all are related to the growing influence of transnational corporations on the global food system. These TNCs are the lead actors in most global food production systems, dictating what is produced, how it is processed, where it is sold and the desirability of food products to global consumers. Looking at these TNCs through the lens of global value chain analysis can begin to shed light on the global and local interactions that are contributing to changing food consumption patterns. These TNCs have come to dominate the global food value chain by operating globally to promote efficiency as well as locally to take advantage of regional preferences. A global value chain perspective highlights the role of TNCs in increasing the availability, affordability and desirability of diets higher in fat and sugar in Malaysia. These diets are scientifically linked to a higher risk for noncommunicable diseases such as obesity, diabetes and cardiovascular disease and Malaysia has experienced rising rates of noncommunicable disease. Because developing nations do not have the financial or medical capacity to deal with these rising rates of noncommunicable disease, the nutrition transition could lead to a global public health crisis. To avoid this kind of crisis, future GVC research should identify intervention points in global food value chains that can reverse this trend and encourage global TNCs to positively influence local diets in the future.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".