Bibliographic record
Abstract
<JATS1:p>Across the globe, people are challenging the agro-industrial food system and its exploitation of people and resources, reduction of local food varieties, and negative health consequences. In this collection leading international anthropologists explore food activism across the globe to show how people speak to, negotiate, or cope with power through food.</JATS1:p> <JATS1:p>Who are the actors of food activism and what forms of agency do they enact? What kinds of economy, exchanges, and market relations do they practice and promote? How are they organized and what are their scales of political action and power relations? Each chapter explores why and how people choose food as a means of forging social and economic justice, covering diverse forms of food activism from individual acts by consumers or producers to organized social groups or movements. The case studies embrace a wide geographical spectrum including Cuba, Sri Lanka, Egypt, Mexico, Italy, Canada, France, Colombia, Japan, and the USA.</JATS1:p> <JATS1:p>This is the first book to examine food activism in diverse local, national, and transnational settings, making it essential reading for students and scholars in anthropology and other fields interested in food, economy, politics and social change.</JATS1:p>
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".