Causes for reforestation failure in Haiti and residents' willingness to pay for cleaner cookstoves
Bibliographic record
Abstract
Abstract Trees provide services to human beings and protect the environment. This study investigates the causes of the failure of reforestation projects in the North and Northeast departments of Haiti. Two questionnaires with closed- and open-ended questions were used for face-to-face and semi-structured interviews with local and non-local authorities, respectively. The test of proportions was used for the statistical analysis, where a result was considered significant when the p-value was less than 0.05. The results showed that 86.8% of the non-local authorities were used to participating in projects of reforestation in their localities. The lack of follow-up and participation of residents in decisions about the type of trees planted were the main causes of the failure of these projects. The interviewees were accustomed to cutting trees to produce charcoal (95.8%) and enlarging their gardens (70.8%). However, 90.0% of each category would invest in purchasing cleaner cookstoves and stop using charcoal if the government agreed to finance up to 50.0% of such a project. The findings of this research could help both the decision-makers and the Haitian government to understand the causes of the failures of reforestation projects in Haiti and adopt an effective way to reduce deforestation in the country.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".