After Nairobi: can the international community help to develop health promotion in Africa?
Bibliographic record
Abstract
The Nairobi Conference presaged a surge of interest in the development of health promotion in sub-Saharan Africa. A number of Africans have asserted that health promotion is underdeveloped in the continent, with the principles of the Ottawa Charter not widely adopted. This paper does not presume to say how health promotion could be developed in Africa, as that is for Africans to decide for themselves. Rather, it debates some issues which the international epistemic health promotion community could address in order to work in solidarity with African colleagues in taking forward the health promotion agenda in their continent. These issues include the Eurocentric nature of health promotion discourse, the different disease burden of Africa and the lack of training capacity in African universities.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 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 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".