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Protecting Childrenʼs Environmental Health: Are There Any Governance Instruments?

2006· article· en· W2071334162 on OpenAlexaffabout
D Spady, Colin L. Soskolne, Irena Buka, Nola M. Ries, Brian D. Ladd, Álvaro Osornio-Vargas, Leonie Nemer, R Bertollilni

Bibliographic record

VenueEpidemiology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLegislationEnforcementEuropean unionCorporate governanceBusinessLaw enforcementEnvironmental healthPolitical scienceGeographyEnvironmental planningMedicineLawFinance

Abstract

fetched live from OpenAlex

MAA2-PD-01 Session Title: Children’s Health and the Environment Introduction: We sought to find governance instruments (GI), that is, laws, regulations, and guidelines, addressing specifically the issue of children's environmental health (CEH) in Organization of Economic Cooperation and Development (OECD) countries and to analyze the GIs as to type, application, goals, implementation, enforcement, and effectiveness. The ultimate purpose is to inform policy that will further Canada's commitment to children's environmental health. Methods: We searched legal databases in OECD countries, plus the websites of state, national, and supranational agencies and nongovernment organizations (NGOs) searching for GIs and grey literature (GL) related to GIs and CEH. We contacted, by e-mail, regular mail, and telephone, individuals that were knowledgeable about CEH and GIs. Our final analysis was limited to GIs specifically addressing issues of the physical environment and CEH GL was used to help us in assessing enforcement and effectiveness. Children were defined as humans from conception to the age of 18. Results: We found nearly 700 GIs, approximately 350 from Europe. Many European GIs were duplicates of Directives created by the European Union (EU) but were harmonized to country-specific legislation. Only 4 GIs (American) were found that clearly addressed in the text the unique characteristics of children and their specific needs; 2 were directives stating that the unique characteristics and needs of children must be considered in any legislation where children might be affected. We found multiple GIs with a clear CEH focus, but little emphasis on the uniqueness of children (eg, pesticide residue in infant foods), and also laws addressing issues such as air and water quality, which did not mention children in the text of the act but that clearly involved concern for child health. There were no laws addressing CEH and the indoor environment, although children spend more than 80% of their lives indoors. Some legislation was impeded because of scientific uncertainty, which, on occasion, appeared to be driven by the industry. The Precautionary Principle appeared to be applied most frequently in European legislation. Lead legislation appeared to be quite effective in reducing lead poisoning in children throughout the OECD. Discussion and Conclusions: CEH is not well-addressed for the majority of laws relating to the environment. However, children do have unique needs and, by not considering these needs, where appropriate, in legislation, puts the present and future health of the child at increasing risk. Often in GIs, data are lacking regarding the effect of environmental agents on CEH.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.179
Threshold uncertainty score0.357

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0030.003
Scholarly communication0.0070.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0180.002

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.

Opus teacher head0.021
GPT teacher head0.285
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2006
Admission routes2
Has abstractyes

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