Linguistic Features of Pidgin in Stand-Up Comedy in Nigeria
Bibliographic record
Abstract
This study is aimed at looking at how comedians are able to create humour through the use of Pidgin in stand-up comedies. It has been observed Pidgin creates a kind of relaxed environment when it is being used in a social setting because of its informal and non-restrictive nature. This study was carried out by identifying and categorizing the features of Pidgin in selected Nigerian comedy shows, interpreting the contents expressed by the features, and by relating the contents to the humorous opinions as expressed in the comedy shows. The data (five Nigerian stand-up comedy videos where Pidgin was adopted) for this research were downloaded on YouTube channel on the Internet and analysed using Halliday’s Systemic Functional linguistics (particularly the interpersonal metafunction). This was done to reveal how language reflects social relationship between the comedian and his audience and how this language expresses humour. The analysis revealed that pidgin is an informal language, and so its informality creates an equal social relationship in an informal setting which aids laughter. Comedians are able to express humour in Pidgin because it is a no man’s native language, and as such, they could use it creatively to achieve their aim - humour. The unserious and informal nature of the language and its method of presentation make their stories humorous. In conclusion, this study offers sociolinguists and discourse analysts an insight into a field that has not been maximally explored.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".