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Record W30135894 · doi:10.4102/sajp.v72i1.338

The Forgotten Victims: Ethnic Minorities in the Nigeria-Biafra War, 1967-1970

2014· article· en· W30135894 on OpenAlexaff
Arua Oko Omaka

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical and Contemporary Political Dynamics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsIgboEthnic groupPersecutionHistoriographyPolitical scienceHistoryInjusticeGender studiesSociologyLawPhilosophyPolitics

Abstract

fetched live from OpenAlex

The Nigeria-Biafra War att racted enormous international att ention not just for the brutal events of the period, but also because of how the confl ict was interpreted, especially by foreigners. The ghastly images of victims of the war dominated the international media and roused the world’s conscience. The confl ict took a toll on human lives on both the Igbo and the ethnic minorities in Biafra. While the Igbo tragedy was largely perpetrated in Northern Nigeria, that of the Biafran minorities – Efi k, Ijaw, Ogoja, Ibibio – occurred mainly in their homelands. The gory experiences suff ered by the Biafran minorities have largely been neglected in the historiography of the Biafra War. This paper examines the experiences of the ethnic minorities in Biafra during the war between July 1967 and January 1970. It argues that the minorities suff ered a high degree of persecution, molestation, injustice, psychological torture and other forms of suff ering which have not been fully examined in existing literature. The war subjected them to layers of loyalty and disloyalty both to the federal authority and the Biafran government. The paper asserts that these minority groups in Biafra were as much victims of the war as the Igbo. Hence, they should be accorded due recognition in the historiography of victimhood in the Nigeria-Biafra War.

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)0.000
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: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.031
GPT teacher head0.226
Teacher spread0.195 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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