#RollBackRTH: Tactics, strategies and framing in the Right to Health Care Act 2022 debate in Rajasthan, India
Bibliographic record
Abstract
The role and influence of interest groups in the healthcare sector, such as the hospital industry, insurers or physicians, are critical aspects of understanding health politics. Yet, scholarship examining the interests and actions of these actors has been surprisingly limited in health politics scholarship on Global South contexts. In India, national- and sub-national health sector reform debates have gained traction. The country's vast, underregulated and powerful private healthcare sector plays a decisive role in shaping policy outcomes. This study explores the public-facing strategies, tactics and frames used by policy actors in the debate surrounding the Right to Health Care Act 2022 in the state of Rajasthan. We describe a policy conflict in which private healthcare sector coalitions representing diverse constituencies united rapidly to effectively execute their opposition strategy. The opposing coalition deployed multiple approaches concurrently, pairing indirect and direct strategies and tactics and using diverse framing choices to "win" the public narrative and secure a dominant role in the policy process, placing supporting policy actors in a defensive position. Our findings contribute to a growing body of scholarship on domestic health politics in Global South contexts that expands our understanding of interest groups into different institutional and ideational spaces.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".