Agricultural Extension Agents' Use of Learning-Based Extension Methods in Trinidad and Tobago
Bibliographic record
Abstract
Abstract: Agricultural extension agents are highly credited for their roles of providing advice to farmers and supporting their learning and decision-making to improve livelihoods. The use of appropriate methods to promote learning in developing countries, including Trinidad and Tobago, has often been highlighted as a development priority. Nevertheless, agricultural extension agents encounter difficulties in applying new competencies. Understanding and utilising appropriate methods based on farmers’ learning needs is critical. This study sought to investigate extension agents’ use of learning-based extension methods. A survey was conducted with 106 extension agents. Descriptive statistics and logistic regression analysis were used to analyse data. The findings show that male agents prefer Plant Clinics and Farmer Field School learning methods. Social influence and networking among organisations had a significant influence on the use of Discovery Based Learning methods. The positive influence of social pressure motivated the agents. The study recommends supporting facilitative conditions through a coordinated programme and to focus on farmers’ learning as a critical consideration for improving the use and impact of learning-based methods Keywords: Learning-based methods, agricultural extension, extension agent, Trinidad and Tobago
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".