Power Struggle in Ola Rotimi’s Kurunmi and Toyin Abiodun’s The Trials of Afonja
Bibliographic record
Abstract
A major cause of crises in various strata of the human society is power struggle in different layers. Ola Rotimi’s Kurunmi and Toyin Abiodun’s The Trials of Afonja exemplify historical African plays which have been copiously previously studied. However, most of the existing studies on these plays have failed to address the issue of power struggles in these plays, despite the fact that power struggle is central to the plays, and is a cardinal issue which is of urgent currency in African continent and different parts of the globe. Power struggle has put a cog in the wheel of progress of many a country across the globe. This study is an enterprise at filling this gap. It examines the various instances of power struggles in Ola Rotimi’s Kurunmi, and Toyin Abiodun’s The Trials of Afonja. Foucault’s Discourse Analysis provides the theoretical framework of the study. In the two plays examined, there is power struggle among several characters. In Kurunmi, there is power struggle between Kurunmi and Adelu, Kurunmi and the Ibadan chiefs, Ogunmola and Ibikunle, and Ibadan and Ijaye. In The Trials of Afonja, Afonja and Alaafin Aole, Afonja and the Oyo warriors, and Afonja and Alimi are locked in power struggles. In addition, figures in authority habitually use language to express their dominance and request obedience from those subordinate to them. The paper submits that Ola Rotimi and Toyin Abiodun employ power struggles among several characters not only to propel the plots of their plays but also to foreground the historical and socio-cultural backgrounds of the plays.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".