Issue Salience, Party Strength, and the Adoption of Health‐Care Expansion Efforts
Bibliographic record
Abstract
This article studies the joint effects of issue salience and party strength on health‐care expansion efforts in the American states. We contend that Democrats and Republicans fall back on their traditional policy stances when an issue is highly salient, but when it is less so, policy makers move to the more politically practical policy alternatives. We find that when health care is highly salient, Democrat‐controlled states will be more likely to support direct coverage programs, while a Republican‐controlled state will be more likely to support tax incentives. During periods of low‐issue salience, policy makers are more open to pursuing options less consonant with traditional partisan policy preferences to make progress on the issue. This important contribution to the literature indicates that the level of attention an issue receives can not only affect whether effort is made to address the problem, but the substance of the policy too. Este artículo estudia los efectos conjuntos de la relevancia de un problema y la fuerza de un partido respecto a los esfuerzos de expansión de la cobertura médica en los estados de la unión americana. Nosotros argumentamos que Demócratas y Republicanos mantienen sus posturas políticas tradicionales cuando un problema es altamente relevante, de lo contrario, los legisladores toman una alternativa política más práctica. Hallamos que cuando la cobertura médica es altamente relevante, los estados Demócratas serán más propensos a apoyar programas de cobertura directa, mientras que los estados Republicanos serán más propensos a apoyar incentivos fiscales. Durante periodos de poca relevancia, los legisladores están más abiertos a considerar medidas con una menor consonancia política a la de su partido con el fin de lograr avances en el tema. Esta importante contribución a la literatura indica que el nivel de atención que recibe un problema no sólo afecta si se toman medidas para resolver el problema sino la solidez de la política implementada también. Related Articles:“Implementation Theory Revisited . . . Again,” (2009): http://onlinelibrary.wiley.com/doi/10.1111/j.1747‐1346.2009.00174.x/abstract “National Health Insurance in the U.S. and Canada,” (2009): http://onlinelibrary.wiley.com/doi/10.1111/j.1747‐1346.2009.00211.x/abstract “Issue Salience, News Coverage, and Attention Cycles,” (1999): http://onlinelibrary.wiley.com/doi/10.1111/j.1747‐1346.2007.00113.x/abstract
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".