Food Systems under Pressure - Local Practices, Risk Resilience, and Sustainability
Bibliographic record
Abstract
This book explores how food systems respond to mounting pressures of population growth, environmental change, and global market dynamics. <i>Food Systems under Pressure - Local Practices, Risk Resilience, and Sustainability</i> brings together case studies from Asia, Europe, and the Americas to show how local practices, institutional frameworks, and ecological realities shape the future of agriculture and food security. The chapters examine themes such as the role of local food systems in Japan, the modernization of Brazil’s beef chain, resilience strategies in the Republic of Moldova, and the economic logic of Canada’s dairy quota regime. Other contributions highlight systemic risks in the Lancang–Mekong region, the environmental costs of agricultural production, water allocation challenges in Hawai‘i, and due diligence reforms in Italy’s supply chains. The book will be valuable to researchers, policymakers, and practitioners seeking comparative insights into how food systems balance health, resilience, and sustainability under global stress.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".