Thematic Preoccupation of Nigerian Literature: A Critical Approach
Bibliographic record
Abstract
Nigerian literature takes "matter" from the realities of Nigerian living conditions and value systems in the past and present. In the Nigerian society the writer, be it a novelist, dramatist or poet is a sensitive "questioner" and reformer; as all literature in a way is criticism of the human condition obtainable in the society it mirrors. The writer often cannot help exposing the bad and the ugly in man and society. Thus much of Nigerian literature is a deploration of the harsh and inhuman condition in which the majority of Nigerians live in i.e. poverty, misery, political oppression, economic exploitation, excesses of the affluent, liquidation of humane Nigerian traditional values, and all forms of injustices which seem to be the lot of a large majority in most Nigerian societies.In drama, novel, poetry or short - story, the writer's dialogue with his physical and human environment comes out as a mirror in which his people and society can see what they look like. Every image painted by a skillful artist is expressed or put into writing / print, becomes public property and leaves itself open for evaluation by those who read and understand the language and expression. There is therefore a need to identify the thematic preoccupation of Nigeria literature which is the focus of this paper with a view to identifying their peculiarities with textual references.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".