Bibliographic record
Abstract
Purpose – This paper is based on a crop insurance implementation currently undergoing in Haiti. The purpose of this paper is to present the development of a program tailored to rice production in the Artibonite Valley, the challenges and opportunities that are arising from the exercise as well as pitfalls and ways to avoid them. Design/methodology/approach – The Système de Financement et d’Assurances Agricoles en Haïti’s approach for the development of crop insurance is in accordance with 13 concepts considered essential in the implementation of agricultural insurance programs. The case study is presented through each of these 13 fundamental concepts. Findings – The paper provides an insight on challenges any organization will face when implementing crop insurance for smallholder farmers. It points out notably that close collaboration of executing agencies with local partners is essential from data collection through insurance development and delivery and that all participants should receive a specific training tailored to their level of education and understanding. Social implications – Haiti is one of the poorest countries on the planet. Smallholder farmers could benefit a lot from crop insurance. It could help them stabilize their income when facing crop losses due to natural hazards or uncontrollable natural events. Originality/value – This paper fulfills an identified need to share real case studies exposing challenges faced when implementing crop insurance for smallholder farmers.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".