Farmers’ Preferences for 4R Technologies and Practices: A Field Experiment
Bibliographic record
Abstract
The agricultural sector is one of the primary sources of environmental pollution. Scientific studies suggest that some technological solutions and sustainable practices can reduce farmlands’ environmental footprint. However, the adoption of such beneficial management practices (BMPs) is below its optimal level. We will conduct a lab-in-the-field experiment in Ontario, Canada to study whether farmers are more inclined to invest in technological solutions or adopt sustainable practices. In this experiment, we will employ a cost-share Becker-DeGroot-Marschak (BDM) (Becker et al., 1964) auction mechanism that will reveal participants’ willingness to pay (WTP) for technological solutions and sustainable practices. We anticipate that farmers at the 2023 Canada’s Outdoor Farm Show will participate in this experiment and submit their cost-share bids. Additionally, we will examine if the default cost-share bid values and farm or farmer characteristics would impact farmers’ bids.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".