wl-4219-OECMs: Lost Opportunity for Inclusive Conservation?
Bibliographic record
Abstract
The Kunming Montreal Global Biodiversity Framework, under the Convention on Biological Diversity, emphasises rights-based approaches to conservation. Target 3, also known as 30X 30 of the Global Biodiversity Framework envisions achieving conservation goals through equitably governed systems of Protected Areas (PAs) and Other Effective Area-Based Conservation Measures (OECMs), that recognise and integrate indigenous rights. OECMs aim to promote in-situ conservation of habitats outside of Protected Areas and can include a range of ecosystems such as forests, wetlands, and agricultural lands with significant biodiversity. Thus OECMs can be an important means of recognising the conservation efforts of diverse actors, particularly indigenous peoples and local communities. Here we explore the context, opportunities, and challenges of OECMs in India, particularly their potential to support community-led conservation within the current legal context. We focus on the contradictions brought about by the Government of India’s claim in their Sixth National Report submitted to the Convention on Biological Diversity in 2018, that over 20% of India’s geographic area is currently under PAs, by classifying large expanses of India’s different categories of forests and wetlands as PAs under the International Union for Conservation of Nature guidelines. This development quashes the potential of OECMs to recognise conservation efforts beyond the formal PA network, in particular those of indigenous peoples and local communities.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".