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A Brief History of Race, Politics and Division in Trinidad and Guyana

2020· article· en· W3097151387 on OpenAlexvenueno aff
Kahlia Brown

Bibliographic record

VenueCaribbean Quilt · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCaribbean history, culture, and politics
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsRace (biology)Regionalism (politics)Political scienceRacial politicsVotingGovernment (linguistics)Development economicsGeographyPolitical economyEconomic growthGender studiesSociologyDemocracyLawEconomics

Abstract

fetched live from OpenAlex

This essay will act as an analysis of the Indo-Afro racial politics of two west Indian countries: Trinidad and Tobago, and Guyana. I will give the circumstances that led to the migration of large numbers of East Indians as indentured servants to Trinidad and Guyana, specifically. I will also explain how these conditions led to a distinct form of government and society. Through tables of electoral data in Trinidad, the racial voting patterns will be observed, and I will elaborate on how political parties do or do not pander to their respective racial communities. Finally, I will conclude by addressing how the racial divide in these two large Caribbean nations impact Caribbean regionalism on a larger scale.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.301
Threshold uncertainty score0.599

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0080.005
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.269
Teacher spread0.235 · 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 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

Citations6
Published2020
Admission routes1
Has abstractyes

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