How do Small Island Developing States Meet the Sustainable Development Goals?
Bibliographic record
Abstract
States in the Global South are facing a double challenge of achieving socio-economic development while adapting to climate change impacts. This study maps to what extent Small Island Developing States (SIDS) manage to meet the Sustainable Development Goals (SDGs). The SIDS are at the front line of climate change and while they share numerous challenges, the SIDS are also a heterogeneous group containing a great variation in terms of economic development, institutional structures, and factor endowments. This paper complements the existing broader international evaluation of SDG outcomes by highlighting SIDS specifically, a group that has been only sporadically covered in the literature. By improving our understanding of different SIDS’s development status and challenges we hope both to make the group more visible in the global debate and to contribute useful knowledge to the ongoing development work in and between the SIDS themselves. We compare the SIDS development performance, defined as meeting the SDGs, to a Global Average (GA), in the three dimensions of sustainable development – economic, social, and environmental. Our investigation confirms that the SIDS are overrepresented among the countries in the world with the poorest data coverage and shows the magnitude of the problem. Further, in our global comparison, we find that they stand out in three aspects – having relatively low levels of poverty, high levels of adult obesity, and low levels of gender equality especially manifested in the share of women in parliament.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".