Factors Affecting the Willingness to Pay for the Protection of the Di River: an Approach Using the Box-Cox Double Hurdle Model
Bibliographic record
Abstract
The Di River, located in West Africa between Burkina Faso and Mali, is a subject of concern to its users. Using econometric models of choice behavior, the determining factors of local populations’ willingness to pay (WTP) for the restoration of the riverbanks are either individual or collective variables. The latter variables imply that data collection focused on common characteristics of the population rather than intrinsic characteristics. Most determining factors have a positive effect on willingness to pay, which is especially observed with subjective or individual variables and reflects the very moderate investment that local populations are willing to make. However, that is also indicative of the potential to achieve sustainable management in such a way that personal factors contribute to increasing the WTP. In addition, the variable related to the level of education of a respondent reveals a willingness to pay a nonfinancial contribution for the restoration of the riverbanks and sustainable management of the resource.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".