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Record W2120858955 · doi:10.2105/ajph.2015.302660

Debating Water Fluoridation Before Dr. Strangelove

2015· article· en· W2120858955 on OpenAlexafffundabout
Catherine Carstairs

Bibliographic record

VenueAmerican Journal of Public Health · 2015
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of CanadaHarvard School of Dental Medicine
KeywordsWater fluoridationHarmDental decayDentistryFluoridePublic healthOpposition (politics)AsideMagic bulletEnvironmental healthPolitical scienceMedicineOral healthLawChemistryNursingArt

Abstract

fetched live from OpenAlex

In the 1930s, scientists learned that small amounts of fluoride naturally occurring in water could protect teeth from decay, and the idea of artificially adding fluoride to public water supplies to achieve the same effect arose. In the 1940s and early 1950s, a number of studies were completed to determine whether fluoride could have harmful effects. The research suggested that the possibility of harm was small. In the early 1950s, Canadian and US medical, dental, and public health bodies all endorsed water fluoridation. I argue in this article that some early concerns about the toxicity of fluoride were put aside as evidence regarding the effectiveness and safety of water fluoridation mounted and as the opposition was taken over by people with little standing in the scientific, medical, and dental communities. The sense of optimism that infused postwar science and the desire of dentists to have a magic bullet that could wipe out tooth decay also affected the scientific debate.

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)0.007
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0040.007
Scholarly communication0.0030.006
Open science0.0010.002
Research integrity0.0100.027
Insufficient payload (model declined to judge)0.0040.001

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 head0.058
GPT teacher head0.353
Teacher spread0.296 · 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.

Study designQualitative
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

Citations28
Published2015
Admission routes3
Has abstractyes

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