Neo-Traditional Medicines: Ethnographic Contributions to Conceptual Definition
Bibliographic record
Abstract
Since the end of 1970, the World Health Organization has encouraged the development of public policies that expand the approach to care and the therapeutic possibilities offered by its member states beyond technoscientific health care. In Brazil, the institutionalization of this approach is related to the promotion of popular and traditional knowledge associated with the usage of medicinal plants. With this convergence as an argumentative horizon, in this ethnography we examine the institutionalization of pharmaceutical services that have become known in Brazilian public health policy as living pharmacies. This term has been mobilized throughout the history of phytotherapy in Brazil and refers to the possibility of instituting the use of medications that expand care approaches and problem resolution possibilities beyond the domain of the biomedical sciences, evoking alliances with so-called traditional and popular knowledge and practices. For this, we propose and discuss the concept of neo-traditional medicines as a comprehensive-interpretative category, verifying the approximation and distancing points assigned to it in contemporaneous anthropological literature. Beyond the domain of science over other fields of knowledge, we argue in favour of this category in order to present new arrangements and social dynamics that define Brazil’s medication policies.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.048 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".