Farmer social networks: The role of advice ties and organizational leadership in agroforestry adoption
Bibliographic record
Abstract
With the decline in public budgets for agricultural extension support, ties between members of farmer groups are becoming more important to facilitate information transfer about agroforestry. This paper examines the role of social network ties in predicting organizational leadership in an agroforestry-based farmer group. Using social network data derived from interviews with members of farming groups based in the Ayeyarwady Delta of Myanmar, we established a positive relationship between advice-seeking ties and organizational leadership. In other words, farmers who were highly sought for agroforestry advice were more likely to be elected as leaders of the farmer group. Results show the frequency of interactions through advice-seeking ties also had a positive influence on the probability of farmers holding leadership positions. We found a core-periphery structure for the advice networks, whereby farmer leaders were overrepresented at the network core. Interestingly, general members of the farmer group were also in the core of the core-periphery structure, suggesting that engaging with farmers without leadership roles can also effectively disseminate agroforestry information to peripheral farmers. We conclude that farmer groups are valuable in agroforestry adoption and persistence and further analyses of formal leadership structures are needed to support more transparent and accountable governance.
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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".