Negotiating Change: Ideas, Institutions, and Political Actors in Tobacco Control Policy Making in Mauritius
Bibliographic record
Abstract
CONTEXT: This article explains the policy process that occasioned the development of comprehensive tobacco control policies in Mauritius from 1980 to 2019. It does so by drawing theoretical insights from John Kingdon's streams framework, historical institutionalism, and ideational perspectives to explicate how tobacco control rose to the status of government policy agenda. METHODS: The main sources of data are government documents, media reports, archival studies, grey literature, and published books and articles. These sources were supplemented by key informant interviews with government officials, civil society groups, and other vested interest groups. FINDINGS: This article finds that the prevalence of noncommunicable diseases in the late 1980s, the political commitment of Mauritius's Labour government to comprehensive tobacco control, the institutional legacies of Mauritius's Public Health Act of 1925, and the administrative capacity of Mauritius's Ministry of Health and Quality of Life are the primary factors that drove tobacco control policies in Mauritius. CONCLUSION: The findings from this study will enrich our understanding of policy change and the politics of tobacco control in the global south. Future research should investigate why some countries in Africa have failed to adopt comprehensive tobacco control policies despite ratifying the Framework Convention on Tobacco Control.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".