The Role of Uncertainty in Regulating E‐Cigarettes: The Emergence of a Regulatory Regime, 2005‐15
Bibliographic record
Abstract
Increasingly, the regulation of public health hazards is subject to an appraisal of the risk of harm to various target populations. However, an expanding body of evidence suggests that when faced with a deficit of information concerning a particular public health risk, governments and regulators do very little to address the risk directly. By examining the case of the first ten years of the availability of electronic cigarettes (2005‐15), this article illustrates a scenario in which regulation to manage public health risks does not occur, because there is insufficient information to support a particular course of action. It then argues that when information is sparse, regulators can find themselves in a zone of uncertainty within which regulatory action can be a significant challenge. Related Articles Givel, Michael S., and Andrew L. Spivak. 2008. “Public Management and the Public Good: The Case of Oklahoma’s 2002 Secondhand Tobacco Smoke Rules.” Politics & Policy 36 (3): 430‐447. https://doi.org/10.1111/j.1747-1346.2008.00115.x Harvey, Olivia. 2009. “Human Embryonic Stem Cell Research in the United States: Some Policy Options for Industry Development.” Politics & Policy 37 (1): 51‐71. https://doi.org/10.1111/j.1747-1346.2008.00161.x Pautz, Michelle C. 2009. “Trust between Regulators and the Regulated: A Case Study of Environmental Inspectors and Facility Personnel in Virginia.” Politics & Policy 37 (5): 1047‐1072. https://doi.org/10.1111/j.1747-1346.2009.00210.x Related Media Baird, Craig. 2016. “Effects of Vaping Up in the Air; City Plans to Follow Provincial Legislation Regarding Smoking Bans.” The Leader‐Post (Regina, Canada). May 14, A8. Britton, John, and Ilze Bogdanovica. 2014. Electronic Cigarettes: A Report Commissioned by Public Health England . London: Public Health England. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/311887/Ecigarettes_report.pdf Galewitz, Phil. 2016. “Here’s What’s Clear as FDA Regulates Vaping.” The Washington Post . No. 05/2016. https://www.pressreader.com/usa/the-washington-post/20160510/283162902808111
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".