Oceans and climate change adaptation: tracking international law and policy developments and challenges
Bibliographic record
Abstract
Climate change threatens the conservation of marine biodiversity, the sustainable use of marine resources, and the human rights of all people, especially those communities that depend on the marine environment for their livelihoods and culture. Sustained, coordinated and ambitious adaptation action is urgently needed. However, adaptation obligations and commitments for the oceans and the ocean economy have largely been addressed within traditionally siloed international regimes. This paper tracks these obligations and commitments by reviewing agreements, decisions and recommendations adopted under five main streams of international law and policy development: climate change, the law of the sea, fisheries and aquaculture, nature conservation, and human rights. The paper focuses on the obligations and commitments of States in two important areas: supporting the resilience of marine ecosystems; and facilitating the adaptation of the fisheries and aquaculture sectors, as representative economic sectors that contribute to food security and sustainable and traditional livelihoods. Through the assessment and review of relevant material, trends, synergies, and challenges have been identified. The paper highlights the evolving content of international law and policy on ocean-based adaptation to climate change. It identifies promising avenues for strengthening the coordination and coherence of ocean-based adaptation, including through the use of common principles, management tools and coordination mechanisms. It also identifies persistent challenges, including implementation gaps, lack of political will, and the complex conceptualization and implementation of adaptation law. The paper concludes by outlining key developments that could facilitate faster and bolder action by States.
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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".