Seeing is Believing? Evidence from an Extension Network Experiment
Bibliographic record
Abstract
Extension services are a keystone of information diffusion in agriculture. This paper exploits a large randomized controlled trial to track diffusion of a new technique in the classic Training and Visit (T&V) extension model, relative to a more direct training model. In both control and treatment communities, contact farmers (CFs) serve as points-of-contacts between agents and other farmers. The intervention (Treatment) aims to address two pitfalls of the T&V model: i) infrequent extension agent visits, and ii) poor quality information. Treatment CFs receive a direct, centralized training. Control communities are exposed to the classic T&V model. Information diffusion was tracked through two nodes: from agents to CFs, and from CFs to others. Directly training CFs leads to large gains in information diffusion and adoption, and CFs learn by doing. Diffusion to others is limited: other males adopt the technique perceived as labor saving, with an effect size of 75 percent.
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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 | 0.021 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".