Bioprospecting and Commercialisation of Biological Resources by Indigenous Communities in India
Bibliographic record
Abstract
Traditional or indigenous science (IS) relates to both the science knowledge of long-resident, usually oral culture peoples, as well as the science knowledge of all peoples who as participants in culture are affected by the worldview and relativist interests of their home communities. Traditional science (TS) has been recognised only lately in Western science as a valuable source of products and treatments for health care. As a consequence, diverse components of IS have been appropriated under intellectual property rights (IPRs) by researchers and commercial enterprises, without any significant compensation to the knowledge’s creators or holders. This paper investigates whether novel forms of commercial uses of biodiversity and associated knowledge carried out by indigenous communities are possible. This understanding will be gained through the examination of the Indian Gram Mooligai Company Limited (GMCL), a community-based enterprise composed of women, which produces and commercialises phytomedicines using the local ethnomedicine knowledge. The paper aims to shows how an alternative representation of bioprospecting at the grassroots level can be an instrument to enhancing the local livelihoods of communities and promoting their empowerment and capacity building. The results show that positive outcomes of this innovative form of participative bioprospecting initiative are evident but that challenges remain.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".