Bibliographic record
Abstract
This thesis examines texts from modern West African literature whose intention is clearly laughter. It reads novels and plays from Chuma Nwokolo, Wole Soyinka, Kobina Sekyi, Ferdinand Oyono, Amadou Hampaté Bâ, Ola Rotimi, and others. The study explores the relationship between writers, their humorous texts, and their audiences. It makes regular reference to theories of humour, but also acknowledges that by the very nature of their imprecise subject, such theories are not at all comprehensive. It does not propose a definition of “African” humour. It does point out, however, that a multilingual context, along with its differences and hierarchies, creates a unique potential for humour. Divided into four main sections, the thesis revolves around character typologies. Chapter One studies modern-day tricksters and argues that their ethos is both amoral and amusing. Chapter Two focuses on mimicry, and how anxious social climbing in a context of colonialism creates humour. Staying within the context of colonialism, while further underlining the relevance of language, Chapter Three examines the figure of the colonial interpreter. The final chapter discusses norms that are foundational for explaining how expectations can be played upon to make us laugh. Though the four are studied separately, I conclude that they are interconnected. Ultimately, the comic figures, in different ways, characterise the unique dialogue that the writer, who is presenting an African story in English or French, is engaged in with his reader. The templates embody a situation that the writer turns into a source of humour with a self-referential quality.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".