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Record W1658728925

Forms of War in Nigerian Literature

2014· article· en· W1658728925 on OpenAlexvenueno aff
Joy M. Etiowo

Bibliographic record

VenueStudies in literature and language · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicPostcolonial and Cultural Literary Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDramaSpanish Civil WarContext (archaeology)PoetryPoliticsLiteratureHistoryLanguage changeLawSociologyArtPolitical science
DOInot available

Abstract

fetched live from OpenAlex

In the Nigerian context, every mention of war (as a word in the Nigerian past or present) automatically takes one back to the Nigerian-Biafran Civil War (1967-1970). While reference to this war is an integral part of this study, this paper examines the different faces of war in Nigeria and literary responses to them. Beyond armed conflict, gender positionings and configurations, political manipulations and intrigues, corruption issues, economic, ethnic and religious-inspired uprisings are wars Nigeria has been, and is still, contending with. From the novels of Chinua Achebe, Elechi Amadi, Chukwuemeka Ike, Isidore Okpewho, Festus Iyayi, Femi Osofisan, Okey Ndibe, Flora Nwapa, Buchi Emecheta, Abubakar Gimba, Tanure Ojaide, Afam Belolisa, Kaine Agary; to the poetry of Wole Soyinka, J. P. Clark, Mabel Segun, Pol Ndu, Peter Onwudinjo, Joe Ushie, Catherine Acholonu, Cecilia Kato, Ibiwari Ikiriko, Sophia Obi; to the drama of J. P. Clark, Wole Soyinka, Ola Rotimi, Arnold Udoka, among others, it is evident that these wars have provided impetus to the Nigerian literary artists. These writers have examined the different facets, phases, implications and prospects of these wars. The underlying lesson, in all, is that the liberative undertone of every/any war must never be abused and/or compromised for selfish purposes or unattainable goals.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.435

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 studies0.0000.000
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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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