A review of ethnobotany and ethnopharmacology of traditional medicines used by Q’eqchi’ Maya Healers of Xna’ajeb’ aj Ralch’o’och’, Belize
Bibliographic record
Abstract
This review describes an Indigenous-led project run by Q’eqchi’ Maya Healers of Belize meant to strengthen and improve traditional botanical healing. The goals of this project were to conserve medicinal plant knowledge by way of ethnobotanical studies, and to conserve the plants themselves by creating a community ethnobotanical garden. A total of 169 medicinal species were collected in the ethnobotanical survey, which provided unique knowledge on many rainforest species of the wet lowland forest of southern Belize, not found in neighbouring Indigenous cultures. Consensus on plant uses by the Healers was high, indicating a well-conserved, codified oral history. After horticultural experimentation by the Healers, the Indigenous botanical garden provided a habitat for and conservation of 102 medicinal species including many epiphytes that were rescued from forested areas. Ethnopharmacological studies by the university partners showed a pharmacological basis for, and active principles of, plants used for epilepsy and anxiety, for inflammatory conditions such as arthritis, for dermatological mycoses, and for type 2 diabetes complications. Overall, the project has provided a model for Indigenous empowerment and First Nation’s science, as well as establishing traditional medicine as an important, unified healing practice that can safely and effectively provide primary health care in its cultural context.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".