Agricultural Extension Agents' Use of Learning-Based Extension Methods in Trinidad and Tobago
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
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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