Decision–making and innovation among small–scale yam farmers in central Jamaica: a dynamic, pragmatic and adaptive process
Bibliographic record
Abstract
Many researchers in the Caribbean have protested the generally negative stereotyping of small–scale farmers and the small–scale domestic agricultural sector. The essence of this pejorative attitude is that small–scale farmers display apathy and resistance to change and are reluctant to accept innovations. A major reason for this perspective is a lack of knowledge and understanding of and sensitivity towards the factors that influence and inform farmers’ decisions. Studying the decision–making of small–scale farmers can, therefore, shed light on their activities and help inform policymaking. This paper uses the example of small–scale yam farmers in central Jamaica to explore and investigate important issues related to decision–making innovations around four questions. Can the decisions of farmers about innovations be considered to be rational? What are the major factors that influence decision outcomes? Why do so many agricultural innovations and modernization initiatives that target small–scale farmers fail? Do farmers really shun innovations that have clear and obvious benefits and, if so, why?
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".