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Record W1991257448 · doi:10.1080/10937400701876293

Risk Communication of Endocrine-Disrupting Chemicals: Improving Knowledge Translation and Transfer

2008· article· en· W1991257448 on OpenAlexaff
Michael G. Tyshenko, Karen P. Phillips, R. Poirier, William Leiss

Bibliographic record

VenueJournal of Toxicology and Environmental Health Part B · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsUniversity of SaskatchewanInstitute of Population and Public HealthUniversity of Ottawa
Fundersnot available
KeywordsTrustworthinessRisk communicationKnowledge transferPerceptionComputer scienceEndocrine systemKnowledge managementPublic opinionSociology of scientific knowledgeUncertaintyRisk analysis (engineering)Internet privacyPsychologyBusinessMedicinePolitical scienceSociology

Abstract

fetched live from OpenAlex

Public perception of the negative effects of endocrine-disrupting chemicals appears to be higher compared to other chemical pollutants, due to (1) chronic, low-probability effects, and (2) uncertainties about which biological effects may be relevant for human health. Individuals, both expert and lay public, require credible, trustworthy, and understandable information about the scientific evidence of endocrine-disrupting chemicals in order to make informed risk decisions. The creation of a dedicated web site, http://www.emcom.ca, as a tool for knowledge translation and transfer provides the general public with access to scientific experts and bridges the gap between experts and nonexperts through a two-way, interactive communications approach. By obtaining accurate and credible information, individuals can make better-informed decisions concerning endocrine-disrupting chemicals.

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)0.082
metaresearch head score (Gemma)0.237
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.082
Threshold uncertainty score0.436

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0820.237
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0030.002
Scholarly communication0.0100.017
Open science0.0030.013
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0250.006

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 head0.066
GPT teacher head0.352
Teacher spread0.285 · 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 designTheoretical or conceptual
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

Citations18
Published2008
Admission routes1
Has abstractyes

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