Exploring the Fences Pertaining to Non Espousal of Traditional Knowledge Based Medicines at Shervaroy Hills, Eastern Ghats, India
Bibliographic record
Abstract
This paper reports on an exploratory research work carried out aiming to identify the barriers in the usage of traditional medicines by the holders of the knowledge. The focus is on the usage of the traditional medicines by the homogenous people, Malayali tribe community at Shervaroy hills of Eastern Ghats. Semi-structured interviews were conducted among the tribe community of the area. Where the knowledge has been passed on from generations making the traditional medicinal knowledge strongly imbibed in their culture, the research findings divulged the reasons as to why in spite of having immense knowledge on traditional medicines these tribe community are opting for codified from of medicines. This report also explored the understanding of older generation in the context of holders of the traditional medicinal knowledge among the Malayali tribe community. The conclusion supports the implementation of existing policies stringently with recommendations so as to draw closer towards the three objectives enshrined in the Convention of Biodiversity and Biological Diversity Act, 2002. This paper contributes to the policy makers, pharmaceutical companies, non-governmental organizations and the holders of traditional medicinal knowledge so as to collaborate, in the process, protecting and promoting the traditional knowledge in medicinal plants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".