Conversational Strategies in Ola Rotimi’s The gods Are Not to Blame
Bibliographic record
Abstract
This paper examined the conversational features used by characters through talk in Ola Rotimi’s The gods Are not to Blame. Several studies have been carried out on Ola Rotimi’s works but this study was motivated by the scanty scholarly studies on the conversational strategies used in the text. In order to bring out the features of conversation in the text, the text was critically read, and salient conversational features were identified. The features were interpreted according to the messages they expressed in the text. The findings revealed that conversational features such as monologue, turn taking, turn allocation, speech overlap, error and repair mechanism, adjacency pairs, and insertion sequence were used to generate different effects in the text. The study concludes that the conversational features employed by the author create orderliness and regulate participants’ talk in the interaction towards actualising the thematic goals of the text. Thus, the study indicates that conversation analysis gives a deeper and better understanding of human utterances as portrayed through the characters in the text, thereby increasing the readers understanding of the text.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".