Lexico-semantic Analysis of Sam Ukala’s Skeletons: A Collection of Storie
Why this work is in the frame
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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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it