Politics, Science, and Termination: A Case Study of Water Fluoridation Policy in Calgary in 2011
Bibliographic record
Abstract
Abstract Policy termination is identified as a rare occurrence and thus difficult to study. However, one policy area, community water fluoridation, has seen an apparent increase in termination in recent years. We examine the specific case of termination in Calgary, Alberta in 2011 with a specific goal to apply Kingdon's Multiple Streams Approach to the policy termination framework. Our findings suggest that of key importance for the termination of water fluoridation was the impending need for an upgrade to the fluoridation infrastructure, the effectiveness of the local anti‐fluoridation activists, the speed of decision making, and a prominent framing of the issue in ethical terms. The opening of a policy window made possible by the 2010 Calgary municipal election, one that introduced a number of new members to council, as well as the presence of a policy entrepreneur who took advantage of the window's opening, were of specific importance to the success of policy termination.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.034 | 0.014 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".