Ethnomedicinal Practices, Challenges and Future Ethnobotanical Implications of the Dongria Kondh tribe of South Western Odisha, India
Bibliographic record
Abstract
The Dongria Khondh, an indigenous tribal community inhabiting the Niyamgiri Hills of Odisha, India, possesses a rich body of traditional ethnomedicinal knowledge deeply rooted in their ecological relationship with the forest. Dependent on diverse forest flora, Dongria Khondh healers utilize a wide range of plant species, including stems, roots, leaves, barks, and fruits, to treat a spectrum of human ailments in the absence of accessible modern healthcare. Ethnobotanical studies in the region have documented the use of over 50 medicinal plant species from numerous botanical families to address common conditions such as gastrointestinal disorders (diarrhoea, dysentery), respiratory complaints (cold, cough, asthma), skin diseases, fever, piles, and wound healing, as well as more chronic issues like diabetes, infertility, and insect bites. Medicinal plants used by the tribe are often prepared as pastes, decoctions, or poultices by local practitioners known as Kabirajas or village healers. This indigenous therapeutic system reflects not only a comprehensive understanding of local biodiversity but also cultural practices that transmit medicinal knowledge across generations, highlighting the critical role of tribal ethnomedicine in community health and biodiversity conservation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".