The Role of Uncertainty in Regulating E‐Cigarettes: The Emergence of a Regulatory Regime, 2005‐15
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it