Towards a framework to support coastal change governance in small islands
Bibliographic record
Abstract
SUMMARY Small islands can guide visualization of the diverse information requirements of future context-relevant coastal governance. On small marine islands (<20 000 km2), negative effects of coastal challenges (e.g., related to population growth, unsustainable resource use or climate change) can develop rapidly, with high intensity and extreme impacts. The smallest and most remote islands within small-island states and small islands in larger states can be threatened by intrinsic governance factors, typically resulting in access to fewer resources than larger islands or administrative centres. For these reasons, efforts to support coastal change governance are critical and need to be targeted. We propose a conceptual framework that distinguishes key governance-related components of small-island social–ecological systems (SESs). To prioritize areas of vulnerability and opportunity, physical, ecological, social, economic and governance attributes are visualized to help show the ability of different types of small-island SESs to adapt, or be transformed, in the face of global and local change. Application of the framework to an Indonesian archipelago illustrates examples of local rule enforcement supporting local self-organized marine governance. Visualization of complex and interconnected social, environmental and economic changes in small-island SESs provides a better understanding of the vulnerabilities and opportunities related to context-specific governance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".