Typical islands, borrowed islands: Epistemological and intellectual decolonialization in island studies
Bibliographic record
Abstract
In the emerging scene of non-western island studies, research done on the sinophone world mainly (but not exclusively) by sinophone scholars has become a conspicuous strand in the literature. Despite the diverse locations in focus, most research tends to functionally apply rather than organically engage with west-inflected island theories. Starting from this premise, I embark on a series of critical reflections on the multilayered and intersecting subjectivities of the non-western island researcher and the related challenges in the context of epistemological decolonialization by analyzing two symptomatic narrative vignettes abstracted from my own research experiences in local island and archipelagic areas in China. Specifically, I argue that in the face of intersecting structures and conflicting forces, the non-western island researcher is both stranded and enabled by a series of in-between conditions in which epistemological innovation is difficult, but still possible if pursued through relentless interrogation of one’s own vested interest and constant realignment of positionality. In the end, I propose three ethical-epistemological traps for non-western scholars practicing island research under the shadow of western intellectual hegemony.
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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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.132 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| 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 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".