Making food-related health taxes palatable in sub-Saharan Africa: lessons from Ghana
Bibliographic record
Abstract
Amidst high burden of infectious diseases, undernutrition and micronutrient deficiencies, non-communicable diseases (NCDs) are predicted to become the leading cause of death in Ghana by 2030. NCDs are driven, to a large extent, by unhealthy food environments. Concerned, the Ghana Ministry of Health (MOH) has since 2012 sought to garner the support of all to address this challenge. We aimed to support the MOH to address the challenge through public health policy measures, but would soon be reminded that longstanding challenges to policy development such as data poverty, and policy inertia needed to be addressed. To do this, the we generated the needed evidence, curated the evidence, and availed the evidence to Ghanaian policymakers, researchers and civil society actors. Thus, we addressed the problem of data poverty using context-relevant research, and policy inertia through advocacy and scholar activism. In this paper, we share how a public interest coalition used context-relevant research, evidence-informed advocacy and scholar activism to valorise and increase demand for healthy food policy (including food-related health taxes) in Ghana.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".