The Politics of Air Bag Safety: A Competition Among Problem Definitions
Bibliographic record
Abstract
In light of 121 deaths attributed to air bag deployments, mainly to children and adults of small stature, recent policy debate has focused on modifying current Federal automotive air bag regulations. A problem definition perspective is employed to understand the nature of this debate. Utilizing a content analysis of the official record of one U.S. House and two U. S. Senate hearings, it is argued that four problem definitions characterize the debate over air bag safety: behavioral, regulatory, technological, and corporate greed. Furthermore, it is argued that a problem definition perspective offers a better explanation of recent changes to Federal air bag regulations than do pluralist, elitist, and principal‐agent models.
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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 | 0.052 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.012 | 0.072 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".