Realizing “30 × 30” in India: The potential, the challenges, and the way forward
Bibliographic record
Abstract
Abstract Of the goals and targets specified by the Kunming‐Montreal Global Biodiversity Framework, Target 3, often referred to as “30 × 30,” has garnered widespread attention globally. In this paper, we critique India's potential to meet this target. We find that with its vast network of ecosystems that are under some form of protection and through the recognition of other effective area‐based conservation measures sites, India has the potential to meet the quantitative target of conserving and managing at least 30% of its area by 2030. However, the qualitative attributes of the target might be more difficult to realize owing to several challenges, such as inadequate landscape connectivity, insufficient representation of habitats in the current protected area model, and the exacerbation of socioeconomic vulnerabilities of resource‐dependent communities. To achieve strategic, inclusive, and equitable conservation, we suggest a four‐pronged approach involving landscape‐level biodiversity conservation, socially just and collaborative safeguarding of biodiversity, and relevant policy (re)formulation, informed and underlain by long‐term research and impact monitoring. Although we focus on India, the issues we discuss are of broader relevance, especially for countries across the Global South that are also likely to be significantly impacted by the implementation of the target.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".