Local Food Systems under Global Influence: The Case of Food, Health and Environment in Five Socio-Ecosystems
Bibliographic record
Abstract
Globalization is transforming food systems around the world. With few geographical areas spared from nutritional, dietary and epidemiological transitions, chronic diseases have reached pandemic proportions. A question therefore arises as to the sustainability of local food systems. The overall purpose of this article is to put in perspective how local food systems respond to globalization through the assessment of five different case studies stemming from an international research network of Human-Environment Observatories (OHM), namely Nunavik (Québec, Canada), Oyapock (French Guiana, France), Estarreja (Portugal), Téssékéré (Senegal) and Littoral-Caraïbes (Guadeloupe, France). Each region retains aspects of its traditional food system, albeit under different patterns of influence modelled by various factors. These include history, cultural practices, remoteness and accessibility to and integration of globalized ultra-processed foods that induce differential health impacts. Furthermore, increases in the threat of environmental contamination can undermine the benefits of locally sourced foods for the profit of ultra-processed foods. These case studies demonstrate that: (i) the influence of globalization on food systems can be properly understood by integrating sociohistorical trajectories, socioeconomic and sociocultural context, ongoing local environmental issues and health determinants; and (ii) long-term and transverse monitoring is essential to understand the sustainability of local food systems vis-à-vis globalization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".