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Record W4367336432 · doi:10.1007/978-3-031-24271-7_12

Narratives and the Water Fluoridation Controversy

2023· book-chapter· en· W4367336432 on OpenAlexaffabout
Andrea M. L. Perrella, Simon Kiss, Ketan Shankardass

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsWater fluoridationNarrativeNormativeOpposition (politics)Public opinionPolitical sciencePublic relationsPsychologySociologyMedicineMedia studiesLawFluorideArtPoliticsLiteratureChemistry

Abstract

fetched live from OpenAlex

Abstract Fluoridation is one of the most significant public health measures of the last century and yet also deeply controversial. Adding a small amount of fluoride in drinking water is a safe and relatively cheap approach to provide oral health in communities. But since its advent in the 1940s, there has been opposition to fluoridation, with a recent resurgence challenging some communities to stop the practice. The aim here is to explore some reasons why this happens, focusing on how different narratives can affect how people think about fluoridation. Some narratives are based on scientific fact, some on normative frames. Is each equally capable of affecting public opinion? Answers are sought through experimental survey questions whereby respondents are exposed to different narratives. This survey was administered in 2017 in both Canada and the United States, with a sample of more than 3400, possibly the largest survey that focuses on attitudes toward water fluoridation. Results suggest that although there is majority support for fluoridation, it is much easier to reduce that support than it is to increase it.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.893
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.404
GPT teacher head0.414
Teacher spread0.010 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations2
Published2023
Admission routes2
Has abstractyes

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