India’s Biological Diversity (Amendment) Act, 2023 in the Era of the Kunming–Montreal Global Biodiversity Framework: An Appraisal
Bibliographic record
Abstract
India amended the Biological Diversity Act, 2002 in August 2023 to recalibrate compliance, access, and benefit-sharing (ABS) with an explicit push to ease domestic R&D and the AYUSH economy while aligning with evolving CBD rules on digital sequence information (DSI). This paper critically evaluates the amendment’s core changes—decriminalization, modified ABS scope, exemptions for codified traditional knowledge and AYUSH practitioners—and situates them against CBD obligations (Nagoya ABS, Cartagena Biosafety) and the Kunming–Montreal Global Biodiversity Framework’s (KM-GBF) goals/targets. It analyses India’s stated positions at COPs, the emergence of the multilateral DSI benefit-sharing mechanism and the Cali Fund, and India’s updated NBSAP/National Targets for 2030, assessing convergence and friction points for communities, innovators, and regulators. Using doctrinal legal analysis, policy tracing, and stakeholder mapping, the paper tests whether India’s “ease-of-doing-research” turn can still deliver fair, equitable benefit sharing and robust conservation finance—especially for IPLC custodians—under GBF implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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 teacher head, 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".