Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".