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Record W4312882098 · doi:10.24043/isj.401

Worlds apart: Island identities and colonial configurations in the Dutch Caribbean

2022· article· en· W4312882098 on OpenAlexvenueno aff
Jessica Vance Roitman, Wouter Veenendaal

Bibliographic record

VenueIsland Studies Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsColonialismJurisdictionPoliticsNationalismGeographyMetropolitan areaGenealogyEthnologyEconomic geographyHistoryPolitical scienceLawArchaeology

Abstract

fetched live from OpenAlex

Analyses of the Caribbean continue to divide the region based on colonial heritage, which is largely a result of the fact that it primarily consists of small islands. In this article, we demonstrate the inaccuracy of such categorizations on the basis of two sets of arguments. Based on historical as well as contemporary evidence from the Dutch Caribbean, we show that different islands that were artificially united into a single jurisdiction by colonial powers commonly experience intense inter-island rivalries and separatist tendencies. In addition, however, we show evidence pointing to frequent contacts and shared regional identities between neighboring islands belonging to different (post)colonial spheres of influence, both in the past and in the present. In sum, therefore, our interdisciplinary analysis – combining insights from history and political science – shows that small islands experience seemingly contradictory tendencies towards both island nationalism and inter-island cooperation, but that cooperation can only work if it is initiated on the terms of islands themselves, and not determined by colonial rulers or metropolitan states.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.624

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0040.005
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.034
GPT teacher head0.329
Teacher spread0.296 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2022
Admission routes1
Has abstractyes

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