Design of strategies to increase de competitiveness of smallholder chains: field manual
Bibliographic record
Abstract
This manual is the result of several years of action-research in diverse sites of Latin America with producer organizations, local nongovernmental organizations, governmental organizations, and the private sector.Without the efforts and ideas of each of these, this document would not be what it is.We would like therefore to recognize the support we have received from:Communities of the Cabuyal River micro-watershed in Cauca Department, Colombia; -Support organizations grouped in the Comité de Agroindustria Rural de Consorcio Interinstitucional para una Agricultura Sostenible en Laderas (CIPASLA); -Communities of the municipalities of Yorito and Sulaco, Yoro Department, Honduras; -Comité de Microempresa de Comité Local para el Desarrollo Sostenible de la Cuenca del río Tascalapa (CLODEST); and -Members of the Corporación para el Desarrollo Sostenible de Ucayalí (CODESU) of Pucallpa, Peru.-CARE Nicaragua and in particular the RENACER and FAROL projects located in Estelí andMatagalpa respectively.The text you have in your hands attempts to combine the work of these pioneers so that other rural communities can apply and adapt to their own needs what has been learned, and thus contribute to improving their livelihoods.The authors wish to acknowledge the generous support provided by the MINGA Program of the International Development Research Centre (IDRC) of Canada which permitted the development of field activities, and the elaboration of this guide.The questions, doubts, and arguments of many CIAT colleagues have served in a similar fashion , as have the queries of students in the First International Course, "Linking small-scale rural producers to chains: Design of strategies to increase competitiveness," offered jointly with the Center for Competitiveness of Eco-Enterprises of the Centro Agronómico Tropical de Investigación y Enseñanza (CATIE), of Costa Rica.Finally, we would like to acknowledge the constant support of our families and their understanding of our frequent absences.Any errors and omissions are the responsibility of the authors.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.114 | 0.013 |
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".