From Commitments to Action: Advancing Community Rights-Based Approaches to Achieve Climate and Conservation Goals
Bibliographic record
Abstract
A large proportion of the world’s remaining high-biodiversity and carbon-rich lands, forests and waters are held by Indigenous Peoples, local communities, and Afro-descendant Peoples, and a robust body of evidence demonstrates the positive environmental outcomes of their governance of these resources. Growing recognition of these roles and contributions is reflected in a range of international commitments, such as the new language on the rights of Indigenous Peoples and local communities adopted in the Kunming-Montréal Global Biodiversity Framework (GBF), commitments regarding Indigenous and community rights in international forest and climate initiatives, and significant funding pledges from climate and conservation donors. However, translating this emerging support into tangible actions with clear and practical meaning for local peoples remains a serious challenge. Long histories of colonialism, dispossession and fortress conservation have marginalized the rights, governance and knowledge of Indigenous Peoples and local communities, leaving persistent structural barriers that risk undermining global efforts to advance human rights-based approaches to climate and conservation. While paradigms have begun to change, more deliberate and concerted actions will be needed to overcome these barriers and elevate rights and community leadership in responses to the global environmental crisis. Without such transformations, the push to achieve climate and biodiversity goals risks following well-established top-down pathways, leading to further marginalization of those on the front lines, the continued infringement of their rights, and failure to stem climate change and biodiversity loss.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.056 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".