The Right to a Clean, Healthy and Sustainable Environment and the Triple Planetary Crisis: Reflections for Ocean Governance
Bibliographic record
Abstract
Abstract According to the United Nations Environment Programme, the world is fac-ing a triple planetary crisis of climate change, nature loss, and pollution and waste, with grave implications for human well-being. While the triple plan-etary crisis affects both marine and terrestrial ecosystems, understanding of human rights in the ocean governance context is less well developed than that of human rights on land. This is slowly changing, even as the relation-ship between human rights and the environment more generally is being clarified in international law. On July 28, 2022, the United Nations General Assembly (UNGA) adopted a resolution recognizing the human right to a clean, healthy and sustainable environment with 161 States voting in favor and none against. This reflection will contemplate the implications of this recent development in international human rights law for ocean govern-ance at a time of triple planetary crisis. Might UNGA recognition of the right to a clean, healthy and sustainable environment contribute necessary tools to overcome the challenges of triple planetary crisis and ultimately help restore planetary, including ocean, health?
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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".