Incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural Cameroon
Bibliographic record
Abstract
This paper applies a Bayesian approach to incorporate non-data information in estimating the opportunity cost for farmers in rural Cameroon to engage in biodiversity conservation and carbon sequestration efforts. Findings from our field survey reveal that only a small percentage of farmers are willing to participate in environmental protection programmes without compensation. A multidimensional preferences analysis indicates that this behavior may be attributed to a disconnection between environmental values and socioeconomic values. Bayesian analysis of the Tobit model, examining Willingness to Accept (WTA) compensation for agroforestry participation, highlights that factors such as aging, higher educational attainment, and higher socioeconomic status are highly likely to promote pro-environmental behaviors. The estimated opportunity cost of supplying environmental services is 10,775 CFA francs with a standard deviation of 333.6 CFA francs per farmer. These results differ qualitatively from the existing literature, underscoring the relative significance of considering expert knowledge in the interpretation of environmental policies.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".