Workshop Report: Advancing Community Rights in Area-based Conservation
Bibliographic record
Abstract
The Convention on Biological Diversity Conference of the Parties (COP15) held in late 2022 marked the adoption of the Kunming-Montreal Global Biodiversity Framework (GBF) and its main area-based conservation target, which calls for conservation of at least 30 percent of terrestrial, inland water, and coastal and marine areas by 2030 (commonly referred to as 30×30). Central to this target and to the Framework in general was the recognition of the significant contributions of Indigenous Peoples and local communities to biodiversity conservation. The adoption of the GBF has opened new opportunities and risks for implementing its area targets in keeping with community-led and rights-based approaches to conservation (RBAs). With the negotiations now concluded, it is essential to shift attention and action to the implementation and monitoring of RBAs for these area targets, especially at country levels. This also requires building increased mutual understanding and collaboration across key constituencies and organizations that can support rights-based conservation. In this context, Rights and Resources Initiative (RRI), Campaign for Nature (C4N), the ICCA Consortium, and the Global Alliance of Territorial Communities (GATC) organized a one-day workshop during the New York Climate Week, 2023 aiming to mobilize collaboration and action on RBAs for the implementation and monitoring of 30×30 targets. This report compiles the key message of this discussion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".