Climate Change, Sea Level Rise and Protecting Displaced Coastal Communities: Possible Solutions
Bibliographic record
Abstract
Climate change and sea level rise are realities that are upon us and which will profoundly impact the lives and basic rights of millions of coastal residents all over the world. As the law stands both at the international and at certain national levels, the basic human rights of the climate displaced are not adequately protected. This paper identifies two possible displacement scenarios, based on the continued availability/non-availability of land in the face of sea level rise and other climate change impacts; namely, the sinking Small Island Developing States phenomeon, where land disappears and there is no surplus land to support habitation, and all other cases, where the coastal land is battered severely but it can be re-utilized through appropriate adaptation measures or even if coastal frontage land disappears there is still land available inland. On this basis, the paper proposes three possible solutions: (1) bilateral or regional treaties to facilitate resettlement of the inhabitants of sinking Small Island Developing States, (2) appropriate coastal climate change adaptation implemented via integrated coastal zone management and (3) creation of new arrangements under the international climate change regime to provide financial assistance and technological support to respond to both situations. Even though the primary focus of this paper is on coastal communities in South Asia, the lessons that it offers are relevant to other coastal contexts as well.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.013 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".