Greenland and the Pacific Islands: An improbable conjunction of development trajectories.
Bibliographic record
Abstract
Predictions for the future of small islands and island states are often pessimistic. Multiple discontents have followed decolonization. In Pacific island states poverty and inequality have increased, free trade offers few development possibilities, governance is weak and urban biased, and aid dependence has not declined. Economic niches, including ‘sovereignty sales’, have largely failed to emerge. Populations are contracting from outer islands, resulting in unmanageable urbanization in primate cities. One outcome has been rising international migration, along with remittances, as a safety valve and diversification strategy. Selective out-migration and limited return migration have contributed to a skill drain. Yet migration has enabled the periphery to survive, and brought improved welfare. Diaspora engagement and deterritorialization have ensued. Formal development strategies, more international than national, emphasize ‘modernity’ with culture to be abhorred and ignored, yet hybridity offers possibilities for a more equitable and environmentally sensitive sustainable development, where modernity has inherent disadvantages. Even so a combination of migration, selective economic diversification and cultural hybridity, can only be shaped within the difficult context of globalization. Seemingly very different Greenland shares multiple similarities with post-colonial Pacific states, including urban bias, deterritorialization and the marginalization of culture, but especially where ‘welfare colonialism’ has been prevalent. These parallels point to both uncomfortable similarities and lessons, and difficult future decolonization and development trajectories in the Arctic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".