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Record W4386468450 · doi:10.24043/001c.85082

Secessionism in Nevis: Why Have Tensions Eased?

2023· article· en· W4386468450 on OpenAlexvenueno aff
Jack Corbett, Jessica Byron

Bibliographic record

VenueIsland Studies Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
Fundersnot available
KeywordsSecessionPoliticsPolitical economyPolitical scienceMomentum (technical analysis)PopulationLegitimacySociologyEconomic geographyPositive economicsGeographyEconomicsLawDemography

Abstract

fetched live from OpenAlex

Existing studies of secessionism focus predominantly on why these movements gain momentum and persist. A subset of work focuses on why secessionist tensions cease. We contribute to these latter studies by adapting the main theories developed to explain why secessionist agitation occurs, to account for abatement. We focus specifically on the island of Nevis in St Kitts and Nevis, a country that should be a “least likely” case for secession, given its small population, territory, and economy, yet has experienced secessionist agitation for much of the second half of the 20 th century. Since the late 1990s, momentum for secession has subsided. We explain why by reference to rationalist, culturalist and institutionalist arguments. We use an in-depth case study method, drawing on a range of sources, that foregrounds equifinality and concatenation across more than a century of inter-island politics. The findings suggest that all three types of arguments have some explanatory value but each fall short of fully accounting for the ebb and flow of secessionist dynamics. The findings may be of particular interest to multi-island states and territories in the Caribbean. They also offer practical lessons about the importance of policies that promote sectoral integration, encourage sociological linkages, and provide scope for dynamic political settlements.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.644
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.092
GPT teacher head0.391
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 designNot applicable
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

Citations2
Published2023
Admission routes1
Has abstractyes

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