Expertise, Health, and Popular Opinion: Debating Water Fluoridation, 1945–80
Bibliographic record
Abstract
Historians have often painted the 1950s and early 1960s as a time of technological optimism, faith in science and medicine, and belief in experts. In fact, there was more anxiety about medical and scientific progress than has often been acknowledged. This anxiety was perhaps most strongly expressed in one of the most hotly debated issues of the day – water fluoridation. Fluoridation referendums can tell us much about how voters responded to the voices of organized medicine and its critics. Doctors and dentists claimed that water fluoridation was perfectly safe and that it would dramatically reduce the incidence of tooth decay, and yet Canadians repeatedly voted against fluoridation in municipal referendums. There is little question that the 1950s and 1960s marked a high point in scientific optimism and faith in experts, but even then there were cracks in the facade. As with the concern over nuclear fallout and ddt, there was fear about where technology might lead us, concern that doctors and dentists might be influenced by large corporations, and worries that further research would show that there were dangers not yet known. In examining these issues, this article adds to a growing body of literature that no longer sees a significant break between the “conservative” 1950s and the “radical” 1960s, and instead suggests that there is more continuity between these periods than has often been acknowledged.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.018 | 0.025 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".