Legislation by Agenda-Setting: Assessing the Media's Role in the Regulation of Bisphenol A in the U.S. States
Bibliographic record
Abstract
Starting in 2008, debate about potential hazardous effects from exposure to bisphenol A (BPA) migrated from the pages of scientific journals to the U.S. media, regulatory authorities, and state legislatures. In the context of deep scientific conflict about the existence of adverse health effects attributable to BPA, this article asks why it was the case that some state legislatures considered or adopted legislative bans on products made from BPA, whereas others did not. Drawing on existing theories of agenda-setting and policy change via punctuated equilibrium as well as a well-defined methodology (event history analysis), evidence of agenda-setting is presented. Particularly, it is argued that routine and high-impact health coverage was significantly related to the chance that a state legislature considered legislation banning products made with BPA. This was indirectly, but importantly, related to the actual adoption by state legislatures of legislative bans on products made with BPA.
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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 | 0.024 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".