CHILDREN AND THE ENVIRONMENT: VALUING INDIRECT EFFECTS ON A CHILD'S LIFE CHANCES
Bibliographic record
Abstract
Children can face disproportionately greater risk from environmental hazards because they are kids—smaller bodies, faster metabolisms, shorter attentions spans, less knowledge, and fewer resources. Environmental programs that reduce risks to children produce benefits to society that should be adequately represented so policy makers have more information to help them decide which policies are most worthwhile relative to their costs. The open question is just how exactly to value these reductions in risks to children, risks which can arise either from a direct effect on their health, or an indirect effect on their life chances because of illness in other family members or the degradation of the environment. This article focuses on valuing these indirect effects to a child's life chances. The question addressed here is whether standard benefits estimation adequately captures the indirect effects on healthy children. If policy makers presume caregivers make fully informed, rational choices when dealing with adverse family health, indirect effects are already accounted for in revealed and stated values: estimating indirect effects implies double counting of benefits. But if policy makers fear that caregivers face choice without complete information or experience, indirect effects might be understated. Then it becomes constructive to devote resources to explore the importance of these indirect effects.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| 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".