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Record W7132868343

Humour in West African Literature: Four Templates

2019· dissertation· W7132868343 on OpenAlexfundno aff
Adwoa Atta Opoku-Agyemang

Bibliographic record

VenueTSpace · 2019
Typedissertation
Language
FieldArts and Humanities
TopicPostcolonial and Cultural Literary Studies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsEthosComicsContext (archaeology)Relevance (law)ColonialismPoint (geometry)Comic strip
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.030
GPT teacher head0.287
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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