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Record W4210476146 · doi:10.33137/cq.v6i1.36901

Reality of Reparations: An Exploration of Neo-Colonialism, Morality and Control in the Caribbean

2022· article· en· W4210476146 on OpenAlexvenueno aff
Amna Khan

Bibliographic record

VenueCaribbean Quilt · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCaribbean history, culture, and politics
Canadian institutionsnot available
Fundersnot available
KeywordsColonialismWrongdoingMoralityPoliticsPolitical scienceContext (archaeology)Political economyPower (physics)SociologyLawHistory

Abstract

fetched live from OpenAlex

Reparations are widely understood as the process by which compensation is given or amends made for previous wrongdoing. In the context of the Caribbean, it may refer to official actions taken by former colonial powers to acknowledge and recompense states affected by colonialism and slavery. This paper seeks to analyse discussions of reparations in the region and consider how the lack of com- pensation may be perceived as demonstrating that modern power relations are merely repackaged propagations of imperialism. Fundamentally, this paper argues that the notion that colonisation was left behind in the 19th century with the abolition of slavery or mid-20th Century with the political indepen- dence of Caribbean nation-states is a facade and uses reparations discourse as the foremost example of such. This is demonstrated through the following questions: Why do reparations need to be paid? Why are they not being paid? And What needs to change?

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)0.008
metaresearch head score (Gemma)0.011
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 score0.240
Threshold uncertainty score0.477

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0210.056
Scholarly communication0.0170.007
Open science0.0020.008
Research integrity0.0040.006
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 head0.091
GPT teacher head0.357
Teacher spread0.266 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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