Descriptions as a Functional Semantic Tool in Ike’s Our Children Are Coming
Bibliographic record
Abstract
This paper discusses the manner in which Ike, the author of Our Children are Coming uses character-descriptions as a tool for message projection in his novel. It adopts the systemic linguistic approach to the study of texts which bothers much about functionality. It relies on the model designed by Adejare (1992) and Jolayemi (2000) in which texts are bifurcated into First Order and Second Order. In Second Order texts, they assert, there exists a message which is projected through three different levels of meaning projection. Descriptions of characters constitute one of the features used in meaning projection at their third meta-level of interpretation of meaning. The paper discusses the different ways in which characters such as Chu Nwoke, Justice Okpetum, Mrs Edo, Chief Olabisi, Apolonia and Archdeacon Obi were described by the author to project the message of human apostasy in the text, the fact that humans are a combination of good and evil as exhibited in the Nigerian elitist materialism.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".