PLASTIC YAM AND PLASTIC YAM STICKS – PERSPECTIVES ON INDIGENOUS TECHNICAL KNOWLEDGE AMONG JAMAICAN FARMERS
Bibliographic record
Abstract
ABSTRACT Yam farming in Jamaica has been one of the few success stories in agriculture since Independence in 1962. Production is entirely dominated by small farmers who have intensified production systems. Over the last decade yam farmers experienced a ‘yam stick problem’ due to the scarcity, poor quality and high prices of yam sticks. This paper focuses on the content and contextualisation of indigenous technical knowledge among yam farmers. The intrinsic dynamic nature of indigenous technical knowledge is revealed by showing how farmers have adapted their cultivation methods and have themselves innovated new ways of staking yams in efforts to solve the yam stick problem. In effect they have had to rely on their own indigenous knowledge base as a source of new ideas. We discuss a series of alternatives to traditional yam staking methods with a large sample of farmers, including both real and hypothetical examples of externally‐induced innovations. Farmers’ responses to these innovations are reported and analysed in the context of Briggs’ recent review of indigenous knowledge and development issues. Our research suggests that farmer innovation is a normal consequence of coping with farming problems. Further, farmers are not intrinsically unresponsive to externally‐induced innovations, which supports the view that ‘Western science’ and indigenous knowledge are not necessarily bipolar and mutually exclusive knowledge systems. We conclude that indigenous technical knowledge can provide a nexus for research in fostering partnerships with farmers, NGOs and planners in their search for sustainable solutions to the yam stick problem and broader aspects of rural development and resource management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".