MétaCan
Menu
Back to cohort
Record W4293027878 · doi:10.24043/isj.394

Typical islands, borrowed islands: Epistemological and intellectual decolonialization in island studies

2022· article· en· W4293027878 on OpenAlexvenueno aff
Gang Hong

Bibliographic record

VenueIsland Studies Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
FundersNational Office for Philosophy and Social Sciences
KeywordsPremiseShadow (psychology)Context (archaeology)ReinterpretationHegemonyNarrativeEpistemologyArchipelagic stateSociologyInterrogationFace (sociological concept)HistoryAestheticsPhilosophySocial sciencePolitical sciencePoliticsArchaeologyLawPsychologyPsychoanalysis

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.358
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueIsland Studies JournalSame topicIsland Studies and Pacific AffairsFrench-language works237,207