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Record W4410984811 · doi:10.3138/jh-2023-0050

“Aggravated Starvation”: Biafran Diaspora, American Statesmen, Organizations, and Community Responses to Biafra during the Nigerian Civil War

2025· article· en· W4410984811 on OpenAlexvenueno aff
Taiwo Bello

Bibliographic record

VenueJournal of History · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical and Contemporary Political Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsDiasporaStarvationSpanish Civil WarPolitical scienceDevelopment economicsInternal medicineMedicineLawEconomics

Abstract

fetched live from OpenAlex

This article presents two main arguments. First, it maintains that it was hard to prove that genocide occurred in Biafra. Second, against the assumption that the American campaign for Biafra was dominated by American public, it argues that the support received from America by the Biafrans was through the efforts of parties, including Biafrans resident in America, American communities, American organizations, and American political class. The article explores America’s intervention in the Nigerian Civil War. It examines the various ways through which Biafrans in America, American politicians, organizations, and communities backed Biafra in its moment of desperate needs. The starvation that was prevalent in Biafra, triggered by the blockade imposed by Nigeria on the secessionist state, had led to the deaths of Biafran civilians, especially women and children. This development attracted the attention of a large percentage of American population, generating concerns for the victims of the war on the Biafran side. As a result, they mobilized personal funds, communal resources, and pressurized their government to aid Biafrans with relief. Although this demand was granted by the American government, its implementation had some implications for US-Nigeria relations. The Biafran experience offers a unique opportunity for understanding the human rights and humanitarian complexities in the war events happening in many places around the world, especially Africa, the Middle East, and Europe.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score0.624

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 studies0.0010.000
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 head0.017
GPT teacher head0.218
Teacher spread0.200 · 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 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
Published2025
Admission routes1
Has abstractyes

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