Modified listening group method as a knowledge-sharing and learning mechanism in agricultural communities in the Philippines
Bibliographic record
Abstract
Purpose: The aim of this study is to explore if the modified listening group method that originated in Canada can enhance learning and sharing amongst farmers in a group learning setup.Design/Methodology/Approach: The study, participated in by 111 rice farmers, was conducted in Farmer Field School sites of PhilRice in the provinces of Agusan Del Sur, Bohol, Ilocos Norte, Negros Occidental, and Tarlac. It employed a quasi-experimental research design.Findings: Overall, we found that the modified listening group method enables learning of technical concepts and provides avenues for farmers to collectively forward their agenda to the government. Taking on a more critical approach, however, and moving this research forward, we argue that there is a need to scrutinise the types of knowledge shared and muted during the course of exchanges amongst farmers. Several questions relating to power relations in knowledge-sharing are advanced in this research.Theoretical Implications: This paper contributes to addressing the dearth of studies from developing countries on the use of listening groups in agricultural extension.Practical Implications: The study explores and offers a cost-effective strategy to enhance learning and sharing in a group learning setup amongst farmers.Originality/Value: The study explored how an old advisory method like the listening groups may be revived and modified to facilitate learning in a group learning setup.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".