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Record W2077337039 · doi:10.1080/09603120802340842

Multi-jurisdictional expert opinion for improving children's environmental health protection

2009· article· en· W2077337039 on OpenAlexafffundabout
Michael G. Tyshenko, Michelle C. Turner, Lorraine Craig, Daniel Krewski

Bibliographic record

VenueInternational Journal of Environmental Health Research · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsUniversity of WaterlooUniversity of Ottawa
FundersEuropean CommissionCommission for Environmental Cooperation
KeywordsLegislatureJurisdictionEnvironmental healthEuropean unionExpert opinionEnvironmental planningMedicinePolitical scienceBusinessPublic relationsEnvironmental resource managementGeographyLaw

Abstract

fetched live from OpenAlex

Expert opinion from Canada, the United States and European Union countries was solicited to examine the regulatory and non-regulatory approaches used to protect children's environmental health. Thirty-five experts were interviewed by telephone from June 2004 to March 2005 using an open-ended survey questionnaire. Experts were asked to name legislative and non-legislative tools used to protect children's environmental health in their jurisdiction as well as the effectiveness of approaches taken, barriers, facilitators, methods of evaluation, and recommendations for improving children's health protection. A number of common themes were revealed by experts in different countries as well as novel approaches that could be used to improve children's environmental health. Determining what types of governance and non-governance instruments are most effective based on experience from other jurisdictions, allows for the determination of common, effective, policy choice from shared children's health environmental risks. It also provides a broad classification of different approaches that have been used for children's environmental health. Three main areas suggested for strengthening children's environmental health protection included: research and surveillance, institutional organization, and regulatory capacity.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.114
GPT teacher head0.463
Teacher spread0.350 · 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 teacher head, not a consensus.

Study designOther design
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

Citations3
Published2009
Admission routes3
Has abstractyes

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