Exploring Ocean-related Rights in a Transforming World
Bibliographic record
Abstract
Rights-based approaches are proving pivotal as we struggle with the challenges around biodiversity loss and ecosystem degradation. Human rights span a broad spectrum from individual and community-based rights to national and global human rights to healthy oceans, which includes access. Rights of Nature, implicit in many Indigenous communities, have seen growing recognition in recent decades and, in several instances, personhood of nature/parts thereof have been granted legal standing. This introduces a new dimension to management of ocean-based anthropogenic activities. In this paper, we examine potential synergies and trade-offs when aiming for a balance between human rights and ocean-related nature’s rights as we strive towards more desirable futures. We focus on rights-based approaches in managing human-nature interactions in the ocean context, including visions that have been developed and visioning processes that have been undertaken, with a view to transforming ocean-human inter-relations and coexistence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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 teacher head, 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".