Bibliographic record
Abstract
There are many examples of nonmonetary awards which can serve as proxies for social recognition of good agricultural stewardship and conservation behavior. However, the degree to which these awards motivate implementation and sustained use of conservation practices (such as cover cropping) has not been adequately examined. In this study, we used a serious game approach to explore the effect of nonmonetary conservation awards on participants’ agricultural management decisions in an online experiment. Our results show that study participants were highly motivated to implement cover crops on a year-by-year basis by the fictional Ecobadge award, particularly when award thresholds were set at low levels. There was no difference between participants with prior agricultural experience and those without. Although participants who were not motivated to seek the Ecobadge achieved higher mean financial returns, they also had a wider variation in their financial performance as a group. Those who attained the Ecobadge were less risk-tolerant than those who did not. Achievement of the Ecobadge decayed over several rounds of game play, except among participants who planted cover crops on a high percentage (≥50%) of their land, suggesting these participants possessed high intrinsic motivation. This exploration suggests that nonmonetary awards have high potential to serve as motivational tools to increase adoption of cover crops and potentially other agricultural conservation practices, likely as part of a suite of motivational strategies. We suggest that organizations reconsider how they issue these awards. Better integration of awards with opportunities for peer-to-peer recognition among farmers is a promising approach to expand implementation of conservation practices.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.408 | 0.201 |
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".