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Record W2123802935 · doi:10.1163/156920905774270457

Cultural Perceptions of Africans in Diaspora and in Africa on Atlantic Slave Trade and Reparations

2005· article· en· W2123802935 on OpenAlexfundno aff
Robert Dibie, J. Akuma-Kalu Njoku

Bibliographic record

VenueAfrican and Asian Studies · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Issues in South Africa
Canadian institutionsnot available
FundersGovernment of Canada
KeywordsDiasporaCorporationPoliticsArgument (complex analysis)African studiesPolitical scienceMiddle PassagePerceptionSociologyLawGender studiesPsychology

Abstract

fetched live from OpenAlex

Abstract This study examines the cultural perceptions of Africans in Diaspora on the Atlantic slave trade and reparations. It uses a cultural centered model to analyze the perception of Africans in Diaspora about the issue of slavery and reparations. The paper also uses a survey method to explore the perceptions of African-Americans in the United States, Africans living in Europe, and Africans living in the African continent about reparations. It argues that the environmental, religious, occupational, social and political conditions that Africans in Diaspora currently live in will determine their perception of slavery and reparations. Despite this argument, the paper stresses that it is a violation of the established precedence in law that is based on the principle of unjust enrichment to not pay some reparations to the present generation of Africans. This principle stipulates that if a person, a corporation or a country profit from the criminal treatment of a group of people, such a person, corporation or country is subject to the payment of reparations on the basis of unjust enrichment. The study further attempts to explain why it has been difficult for the western industrial world to agree to pay reparations to the children of over 25,000,000 Africans who were wrenched out of Africa as slaves. The concluding section of the paper suggests different reparation methods that would help create a permanent solution that might be acceptable to all.

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)0.000
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 score0.142
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.040
GPT teacher head0.343
Teacher spread0.302 · 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

Citations5
Published2005
Admission routes1
Has abstractyes

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