Translating malaria as<i>sumaya</i>: Justified convention or inappropriateness?
Bibliographic record
Abstract
In exchanges between health professionals and consultants in the West African context, the word malaria is often replaced by its equivalent in the local dialect. In the Nouna health district of Burkina Faso the term malaria is regularly translated as sumaya. Acknowledging that there may be important epistemological differences between malaria, a term issued from the biomedical epistemology, and sumaya, which is borrowed from traditional medicine epistemology, the possible mismatches between these two terms have been assessed to anticipate problems that may result from their translation by different health stakeholders. By consulting various traditional healers and other members of the communities about the local meaning of the term sumaya, it has been possible to compare the conceptualisation of sumaya to the biomedical conceptualisation of malaria and assess the gap between them. An investigation based on a sample of 13 traditional healers and over 450 individuals from Nouna's health district was conducted to document the meaning of the term sumaya. This paper demonstrates that the generally accepted translation of the word malaria as sumaya is a mistake when one looks at the different systems of belief and representations given to each of these two terms.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".