Protection of Bio-resources and Associated Knowledge against Bio-Piracy: A Critical Appraisal of Access-Control Mechanism in Biodiversity Management
Bibliographic record
Abstract
A core intellectual property politics at international level is the tug of war between biodiversity rich nation who complain that intellectual property regime promotes commercialization and exclusivity over their national resources and technology rich nations who term such access to biological resources and privatization of rights over inventions over such resources as bioprospecting.India, with its remarkable biodiversity has been prone to bio-piracy in cases of neem, turmeric and many more.This chapter is an attempt to review the international legal norms relating to biodiversity and compare how access control and benefit sharing mechanisms has been implemented in specific to Indian legal regime.The chapter introduces the challenges in protection of biodiversity in the first section.The second section highlights the necessity of appropriate legal tools for protection of biodiversity emphasizing its need not only for conservation, preservation but also for catering needs of modern society and generation of revenue for a nation.In the third section, an attempt has been made to focus on the salient features of Convention on Biodiversity, its Protocols, and the latest Kunming-Montreal Global Biodiversity Framework (GBF) as adopted in 2022.Indian position on access-control mechanisms and benefit sharing method for biodiversity management has been discussed with illustrative examples and incorporating prominent changes as introduced by Biological Diversity (Amendment) Act, 2023 in the last section.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".