Regionalizing the Sustainable Development Goals for Island Societies: Lessons From Iceland and Newfoundland
Bibliographic record
Abstract
The UN Sustainable Development Goals (SDGs) provide a framework that makes the concept of “sustainable development” more actionable. The nature of island societies — where political jurisdictions overlap in complex ways with land and oceanic ecologies — makes the question of who is responsible for SDG implementation and governance particularly important. We compare SDG interpretations and perceptions of SDG governance in Iceland and Newfoundland using survey and focus group data with stakeholders from government, business, labour, civil society, academia, and youth. Our research questions are as follows: How do research participants view the SDGs in relation to ensuring sustainable futures for their respective island societies? How do research participants view the roles of government and other institutions in implementing sustainability? Answering these questions gives insight into a third theoretically valuable question: Is it the state versus subnational jurisdiction distinction, or is it the common small polity/island dynamics of these cases that is important for understanding the interpretations of the SDGs and their implementation? The interpretations of regionalizing and localizing the SDGs are similar across our two cases, which lends support to a small polity/islandness view of how the SDGs are translated for island societies.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".