Farmer innovation diffusion via network building: a case of winter greenhouse diffusion in China
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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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