Learning from Carchi: agricultural modernisation and the production of decline
Bibliographic record
Abstract
Provided its natural endowments, generally educated rural population, infrastructure and market access to two countries, the Province of Carchi, located in the northernmost highlands of Ecuador, is potentially one of the most productive agriculture regions in the Andes. In the 1960s development experts and the government targeted the region as a model for agricultural modernisation. Following land reform and rapid organisation around industrial era technologies, potato farming in Carchi boomed during the 1970s, evolving to dominate the landscape and become the major source of livelihoods in the province. By the early 1980s, Carchi came to produce nearly half the national potato harvest on less than a quarter of the country’s area dedicated to the crop. In the early1990s, however, production and productivity began to fall off, leading a growing number of rural families in Carchi to fall into debt and abandon potato farming. The research reported here is the outcome of the author’s ten years of research and development practice in Carchi with the International Potato Center, the FAO’s Global IPM Facility, and World Neighbors. It reflects unfolding experience with different phases of hope, discovery, and ambition. Many aspects of the experience have been published elsewhere (see Appendix A). The resulting dissertation is not a case study in the sense of a case that tests a hypothesis. It is a monograph that attempts to produce a single coherent story over seemingly unrelated events, focusing on a second-generation problem: despite a decade of highly rigorous, scientific research on the pathologies of Carchi and multiple public demonstrations of feasible alternatives, little significant change was achieved.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".