Neo‐developmentalism and a “<i>vía campesina</i>” for rural development: Unreconciled projects in Ecuador's Citizen's Revolution
Bibliographic record
Abstract
Abstract From the outside, it appears that the government of President Rafael Correa in Ecuador has put in place a legal and policy framework for a vía campesina model of rural development, inspired by food sovereignty and buen vivir. Recent studies have, however, concluded that a considerable disjuncture exists between this framework and the actual agricultural policies and programmes implemented by the government. In this paper, I provide a broad overview of the agricultural and rural development policies under the Correa government and analyse some of the causes of the gap between the policy framework and policy implementation. I argue that Ecuador under Correa speaks to the difficulties of reconciling a vía campesina approach to rural development with a neo‐developmental economic model. I focus on several issues in particular in order to explain the disjuncture: how the growth of “vía campesina” proposals and political discourse in Ecuador since the 1980s coincided with significant processes of agrarian change; the transformation of rural social movement federations from a sociopolitical force into a political/electoral force and the subsequent decline of these movements; and the deepening integration of small‐scale producers into domestic agribusiness commodity chains and the growth of national agribusiness firms during the Correa government.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".