Revisiting the Debate on Intellectual Property Rights and Traditional Knowledge of Biodiversity: Accommodating Local Realities and Perspectives
Bibliographic record
Abstract
The international and national debates and developments on the applicability of an intellectual property rights regime for protecting traditional knowledge associated with biodiversity is over a decade old. Nevertheless, this continues to be an area fraught with difficulties for many reasons, such as inherent mismatch between the nature of intellectual property rights regimes and that of traditional knowledge, lack of an effective international framework, and alleged lack of will on the part of developed countries. The paper argues that the possible non-inclusion of traditional knowledge holders in the process and the lack of their practical capacity is another key reason for non-effectiveness of existing or envisaged legal instruments. It takes the position that a major lacuna of this discourse is that it is not strongly positioned in the local economic, political, and social contexts in which local and Indigenous communities find themselves today. Using a field-based case study of an Indigenous scheduled tribe, the Karbis in the northeastern state of Assam, the paper makes the case for discarding commonly held, often non-realistic ‘assumptions’ about local and Indigenous communities and accommodation of their realities and perspectives in enacting ‘rights based’ law and policy on these issues.
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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 | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.103 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.010 | 0.015 |
| 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".