A new classification of small island economies based on geography, demography and sovereignty
Bibliographic record
Abstract
We explore and use correlations (not causations) between geographic and demographic characteristics and current levels of sovereignty in order to propose a new classification of small, island and coastal territories. While previous analyses mostly rely on descriptive statistics between the group of UN-members and subnational jurisdictions, we take advantage of a “formal sovereignty” index developed by Alberti and Goujon (2020) that provides a continuous and multidimensional measure of sovereignty or autonomy for a sample of 100 small island states and coastal/island territories. Huge heterogeneity within such a sample leads us to use a data-driven method of principal component analysis and clustering in order to secure a multidimensional typology of small islands relative to their main geographic and demographic characteristics and their level of sovereignty. The PCA results show that heterogeneity is firstly explained by a combination of geographic and demographic variables, and secondly by sovereignty, associated (positively) with population size and (negatively) with insularity. The clustering analysis leads to divide the 100 territories into four clusters mainly characterized by, respectively: Group 1 (32 territories): high sovereignty associated with a large population; Group 2 (26 territories): high values of latitude and life expectancy (mostly Atlantic and Baltic territories); Group 3 (40 territories): large distance to metropolitan power and high insularity (Pacific Regions); and Group 4: Greenland and Nunavut, two territories with a large land area, high latitude, low populations and large EEZ surface area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".