Valuing a reduction in the risk of very low birth weight
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is ample evidence that exposure to various chemicals can increase the probability of children to be born with low or very low birth weight. Infants born with very low birth weight have a higher risk of suffering from neurosensory problems, issues related to behavioural and social competencies, and learning disabilities than infants born with normal birth weight. Authorities face challenges in regulating chemical substances through actions such as bans and prohibitions, because of the difficulty in explicitly considering the economic benefits and costs of such regulations. Moreover, existing Values of a Statistical Case (VSC) of very low birth weight are rare and cannot be directly applied to the cost benefit analysis of chemical management options for a wide range of countries. This paper is part of the series of large scale willingness to pay (WTP) studies resulting from the Surveys to elicit Willingness to pay to Avoid Chemicals related negative Health Effects (SWACHE) project that intends to improve the basis for doing cost benefit analyses of chemicals management options and environmental policies in general. The present paper details a stated preference survey estimating WTP to reduce the risk of very low birth weight, filling an important gap in the valuation literature and addressing a need for applied benefits analysis for chemicals regulation. The SWACHE very low birth weight survey was fielded in 9 countries: Canada, the Czech Republic, Italy, Mexico, the Netherlands, Switzerland, Türkiye, the United Kingdom, and the United States.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it