‘Punch up, punch down o! All I know is there is punch’: Jokes about Africa(ns) in cross-cultural contexts
Bibliographic record
Abstract
Jokes concurrently produce humour and offence owing to differences in cultural considerations of funniness and taboo. With growing audience diversity and online dissemination of live events, stand-up comics are exposed to increased scrutiny for irreverent anecdotes. Yet, ‘punching up’ has become an acceptable form of benign transgression. This is more so in cross-cultural contexts where differences heighten offence, because jokes do not just make us laugh but also create discomfort, especially when the joke-teller is different from us; whether it is ‘up’ or ‘down’, a punch is still a punch. Using the stand-up acts of four African diaspora comedians – Andi Osho, Dave Davis, Urzila Carlson and Trevor Noah – this essay interrogates cross-cultural joke presentation mechanics, themes and performer–audience relations to determine how and why these jokesters variously utilize punch-up jokes. Queries guiding the study include, what performance specificities do humourists enact to mitigate offence while dealing with sensitive/volatile subjects and a more diverse, political correctness-conscious audience? What is/are the relationship(s) between identity, cultural representations and jokes? In answering these questions, the emphasis is on discussing how the selected comedians craftily erect pre-determined sets of values that establish the context(s) within which the offensiveness of their ‘punch(es)’ is/are mitigated.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".