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Record W3090003527 · doi:10.24043/isj.130

Disrupted identities and forced nomads: A post-disaster legacy of neocolonialism in the island of Barbuda, Lesser Antilles

2020· article· en· W3090003527 on OpenAlexaffvenue
Sophia Perdikaris, Rebecca Boger, Edith Gonzalez, Emira Ibrahimpašić, Jennifer D. Adams

Bibliographic record

VenueIsland Studies Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsContext (archaeology)Forced migrationRefugeePoliticsTourismGeographyDevelopment economicsIdentity (music)Political economyRepatriationEthnologyHistoryPolitical scienceSociologyLawArchaeology

Abstract

fetched live from OpenAlex

In the aftermath of the forced evacuation of the island of Barbuda due to Hurricane Irma, the Barbudan people have experienced an exile and return to a ‘new’ geographical, political, and economic context, albeit on the same island. With the specter of climate change and the potential impacts on island communities and nations, we use Barbuda, sister island of Antigua in the Lesser Antilles, to examine the trajectory of nomadic identities as they navigate changes that threaten contemporary land relationships and culture. Since its first permanent settlement in the 17th Century, the island geography of Barbuda has been fundamental to Barbudan identity and provided continuity into modern Barbudan culture. The breaking down of this close relationship with the land and the introduction of a tourism monoculture reduces Barbuda’s ability to respond to crises such as hurricanes and pandemics. In the challenge of a post-disaster economic context, we will address the conditions pushing Barbudans towards a nomadic identity. We will discuss the nomadic in terms of forced exile and subsequent return to an island changed both by a severe weather event and subsequent policy that is disruptive to Barbudan identity, sovereignty, and way of life.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.043
GPT teacher head0.332
Teacher spread0.290 · 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.

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

Citations28
Published2020
Admission routes2
Has abstractyes

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