Globalization and food sovereignty: Global and local change in the new politics of food by Peter Andrée, Jeffrey Ayres, Michael J. Bosia, and Marie-Josée Massicotte (Eds.)
Bibliographic record
Abstract
“To demand a space of food sovereignty is to demand specific arrangements to govern territory and space” (Patel, 2009, p. 667). However, the further we move into a globalized system of food and agricultural production, the more these specific arrangements come into conflict with current global systems of governance. Andrée et al.’s Globalization and Food Sovereignty provides an insightful account of the tensions and complexities of the burgeoning concept of food sovereignty. Its holistic examination of how food sovereignty plays out in both theoretical terms and in practice, in the Global North and South, and at both the local and global levels, serves as one of its greatest strengths. Through a superb set of case studies, it shows how the two themes of food sovereignty and neoliberal globalization interact, manifesting in different ways in different locales and contexts, and at times for different ends. Drawing on contributions from a range of academic disciplines, but directed specifically at political science, this book engages in a theoretically driven analysis of food sovereignty that urges us to take notice of this “new politics of food.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".