Lexico-semantic Analysis of Sam Ukala’s Skeletons: A Collection of Storie
Bibliographic record
Abstract
This study is devoted to Ukala’s use of lexico-semantic devices in Skeletons: A Collection of Stories, to convey the themes of the text. The ability of a literary writer to use the appropriate lexical items and style in a text is expedient for the conveyance of meanings, and the themes of such a text. This is due to the fact that the ideational function of language can only be performed if the readers effectively grasp the subject matter of the text. Every literary artist strives to convey his/her messages in the best possible manner. This study explicates Ukala’s creative strategies and choice of words in his text under study. Due to Nigeria’s complex language problem, which is compounded by the British imposition of the English Language on Nigeria as a result of colonialism, creative writers are constrained creating literature in a second language, which is alien to African culture. To adequately articulate African culture, world-view and their literary visions in their texts, the English language has been domesticated through manipulation and adaptation. Ukala contextualizes English in Skeletons by the deployment of various creative devices, among which are figures of speech, proverbs, idioms, lexical collocation, and neologism. Due to the poetic license which creative writers enjoy, he violates the rules of semantic expectancy, in his linguistic and creative experimentation in Skeletons. This paper identifies and explicates the various lexico-semantic devices Ukala deploys, and their stylistic functions in the text. The study will be of immense contribution to knowledge because it will act as a springboard to researches in the language of African literature.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".