Core Human Rights Principles for Private Conservation Organizations and Funders
Bibliographic record
Abstract
The international community is grappling with an unprecedented loss of biological diversity—a crisis that directly impacts both biodiversity and human rights. The degradation of biodiversity significantly impairs the ability of all people, particularly Indigenous Peoples and communities dependent on natural ecosystems, to enjoy their human rights. States are the primary duty-bearers under international human rights law; however, private conservation organizations and funders are crucial in driving conservation efforts and promoting a human rights-based approach. Despite their significance, acommon understanding of their human rights responsibilities has been largely lacking. To bridge this gap, UNEP is introducing the ten Core Human Rights Principles for Private Conservation Organizations and Funders. These principles guide private actors toward a human rights-based approach to conservation, fostering more inclusive and equitable practices that protect and promote the rights of Indigenous Peoples and others in conservation. The Principles also provide general guidance for all stakeholders on how to center human rights in conservation efforts and contribute to achieving the goals and targets of the Kunming-Montreal Global Biodiversity Framework (KMGBF) through a rights-based approach.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.020 |
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".