Islands of indigeneity: Cultural distinction, indigenous territory and island spatiality
Bibliographic record
Abstract
Islands are often associated with distinct cultures. Although the island polities that formed during the withdrawal of empire frequently brought together various ethnicities, Indigenous governance and claims to cultural distinction have often remained an ideal for such islands and archipelagos. This paper examines the complex causality behind associations between indigeneity and islandness, discussing how island spatiality fosters: (1) cultural distinction, (2) connections between people and place, and (3) Indigenous territory. We argue that islands are exceptionally fruitful spaces for developing and maintaining distinct ethnicities, due not just to material effects of island geography but also in the manner in which both islanders and mainlanders conceptualise islands as “legible geographies.” Islands can thereby become quintessential spaces for containing Indigenous Peoples, simultaneously sustaining cultural difference while limiting the scope for Indigenous self‐determination. Drawing on cases from the Arctic, East Asia, Oceania and the Caribbean, we highlight the benefits that island spatiality can offer to Indigenous communities as well as the dangerous manner in which island spatiality can encourage essentialisations of Indigenous Peoples and circumscriptions of Indigenous spaces. This paper positions itself as an effort in decolonial island studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".