Politics Dressed as Science: Two Think Tanks on Environmental Regulation and Health
Bibliographic record
Abstract
Book Review| April 01 2004 Politics Dressed as Science: Two Think Tanks on Environmental Regulation and Health Laura Jones, ed. Safe Enough? Managing Risk and Regulation. Vancouver, BC: Fraser Institute, 2000. 279 pp. $19.95CDN cloth.S. Robert Lichter and Stanley Rothman. Environmental Cancer—A Political Disease? New Haven, CT: Yale University Press, 1999. 256 pp. $40.00 cloth; $19.00 paper. Alina Gildiner Alina Gildiner Search for other works by this author on: This Site Google J Health Polit Policy Law (2004) 29 (2): 313–322. https://doi.org/10.1215/03616878-29-2-313 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Alina Gildiner; Politics Dressed as Science: Two Think Tanks on Environmental Regulation and Health. J Health Polit Policy Law 1 April 2004; 29 (2): 313–322. doi: https://doi.org/10.1215/03616878-29-2-313 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll JournalsJournal of Health Politics, Policy and Law Search Advanced Search The text of this article is only available as a PDF. © 2004 by Duke University Press2004 Article PDF first page preview Close Modal You do not currently have access to this content.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".