Experiential knowledge, suffering, and vicissitudes of having malaria in the Brazilian Amazon: An ethnographic study
Bibliographic record
Abstract
This paper focuses on the multiple ways in which people who live along the rivers of the Brazilian Amazon, known as ribeirinhos, experience malaria outside of a clinical setting. It describes the local understanding of malaria, strategies to distinguish the illness from other febrile sicknesses, challenges for detecting the disease through biomedical diagnosis methods, and vicissitudes of having malaria. It draws on cases from ribeirinhos from a peri-urban community of Manaus and a rural community from Careiro, State of Amazonas, Brazil. Although malaria is biomedically characterized by the pathogens causing the disease, ribeirinhos have developed other standards to define malaria, including the intensity of the symptoms, the interval between the infections, and the types of medications dispensed to them. In the riverine communities studied, the etiology of malaria includes mosquitoes, microbes, water, wind, sun, and person-to-person transmission. Symptoms of malaria were found to overlap with other febrile sicknesses; hence, ribeirinhos developed skills to monitor how a malaise unfolds in their bodies. Experiential knowledge plays a key role in the early detection of malaria. Individuals who have no previous experience with malaria were found to spend more time seeking health care. Equally important, ribeirinhos perceive that malaria is part of the landscape they inhabit.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".