Biosphere Defenders Leveraging the Human Right to Healthy Environment for Transformative Change
Bibliographic record
Abstract
Earth’s life support systems depend on biodiversity and healthy ecosystems. Without radical transformations, staying within safe planetary boundaries becomes impossible. While the inequalities between Global North and Global South are increasingly acknowledged, the agency and rights of people often placed in the category of “vulnerable” -women, youth, indigenous peoples and local communities- are not sufficiently recognized. This article discusses the role of biosphere defenders in the context of the 2022–2030 Kunming-Montreal Global Biodiversity Framework and the right to a healthy environment. Through dissecting judicial cases, the article investigates promising examples of ways in which biosphere defenders use the law to trigger societal change. This article finds that biosphere defenders contribute to unleashing values of responsibility by various actors, translating biocultural values of ecosystems into evidence in judicial processes impacting bureaucratic and financial systems. Supporting the work of biosphere defenders and placing the right to a sustainable environment at the heart of biodiversity and human rights law will be vital in confronting head-on the planetary crises.
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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".