Valuing malaria morbidity: results from a global meta-analysis
Bibliographic record
Abstract
The risk of malaria transmission worldwide is expected to increase with climate change. In order to estimate the welfare implications, we analyse the factors that explain willingness to pay to avoid malaria morbidity using a meta-analysis. We fail to replicate a previous meta-analysis, despite using a near-identical dataset. Thus, this paper outlines a more robust approach to analysing such data. We compare multiple regression models via a cross-validation exercise to assess best fit, the first in the meta-analysis literature to do so. Weighted random effects gives best fit. Confirming previous studies, we find that revealed preferences are significantly lower than stated preferences; and that there is no significant difference in the willingness to pay for policies that prevent (pre-morbidity) or treat malaria (post-morbidity). We add two new results to the morbidity literature: (1) Age has a non-linear impact on mean willingness to pay and (2) willingness to pay decreases if malaria policies target communities instead of individual households.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".