How information and messengers affect Farmers' cover crop adoption: A field experiment
Bibliographic record
Abstract
To study how information and messengers impact farmers’ willingness to adopt cover crops, we conducted a lab-in-the-field experiment in Ontario, Canada. 564 farmers submitted their bids in a random n-th price auction for four types of cover crop seeds. We randomized two types of messages with emphasis on either the private benefits or the public benefits of cover crops, orthogonal to five different messengers (scientists, non-profit organizations, policymakers, fellow farmers, and private company representatives) via a between-subjects design. This 2 x 5 design is complemented by a control group in which no message and, thus, no messenger was displayed. Our results show for each of the messages, farmers’ average bids were highest when delivered by non-profit organizations, followed by fellow farmers, policymakers, private company representatives, and scientists. A striking finding is that messages attributed to scientists significantly lowered farmers’ bids by 16.2% (95%CI[-31.4%, -1.0%]) compared to the control group.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".