Decision–making and innovation among small–scale yam farmers in central Jamaica: a dynamic, pragmatic and adaptive process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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?
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it