Theorizing Food Sovereignty from a Class-Analytical Lens: The Case of Agrarian Mobilization in Argentina
Bibliographic record
Abstract
Where do the conceptual ambiguities of food sovereignty lie and how can they be overcome? This article identifies a total of five challenges that underlie these ambiguities, namely the challenge of determining how food sovereignty as a research framework can address the tensions between: (a) state–movement relationships, (b) local–national interests, (c) rural–urban conflicts, (d) individual–collective choices and (e) political intermittence–organizational continuity. Using the method of integrative review, I argue that these challenges could be overcome if the criteria for addressing these tensions were based on the interests of the classes of labour by re-envisioning food sovereignty as a social mobilization outcome that potentially leads to agrarian class formation. A class-analytical approach to food sovereignty is thus deployed to study the case of Argentina in order to contribute to a more in-depth theoretical refinement and resolution of the conceptual ambiguities of food sovereignty.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".