Farmer innovation diffusion via network building: a case of winter greenhouse diffusion in China
Bibliographic record
Abstract
Abstract Farmer innovation diffusion (FID) in the developing world is not simply the adoption of an innovation made by farmers, but a process of communication and cooperation between farmers, governments, and other stakeholders. While increasing attention has been paid to farmer innovation, little is known about how farmers’ innovations are successfully diffused. To fill this gap, this paper aims to address the following questions: What conditions are necessary for farmers to participate in FID? How is a collaborative network built up between farmers and stakeholders for this purpose? And what roles can government play? The above questions are addressed through analysis of the diffusion of winter greenhouse technology in China. A framework for analyzing a FID system is developed, and the conclusion is drawn that building mutual trust and collaborative networks is crucial for the success of FID. Furthermore, this network building can be broken down into various levels with different scales, speeds and consequences for FID: informal networks among farmers themselves, farmer-led networks, and government-facilitated networks. The success of government intervention depends upon building and enhancing the collaborative networks in which farmer leadership is crucial.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".