Playing to the out-group: discovering stand-up comedy’s ‘other’ audiences
Bibliographic record
Abstract
Until recently, live joke performances heavily relied on the physical co-presence of comedians and audiences. This mostly exclusive and bounded space facilitated in-group scenarios usually haunted by out-group persons who, though physically absent, are almost always present as joke subjects. More recently, with the advent of accelerated online dissemination, comedy’s ‘other audiences’ have become virtually and physically present and critically engaged in ways that were previously impossible. In this essay, we comparatively analyze Hoodo Hersi’s and Russell Peters’ stand-up routines to highlight the differences between pluralistic and monolithic understandings of audiences. We also underscore the strategic shift inherent in the expansion of the joking space, comedians’ awareness of potential decontextualization, and shifting sensibilities among audiences (Nwankwọ, ‘Shifting Cognitions’), which reject conventional in-group/out-group divisions and counteract the increasingly nebulous and difficult-to-pinpoint practice of ‘punching down’. Building on scholarships about how performers ‘cast’ audiences (Lynch) and how social media affects stand-up comedy (Nwankwọ, ‘Incongruous Liaisons’; ‘Punch up’), we demonstrate how recognition of and increased scrutiny by comedy’s ‘other audiences’ is altering the exclusivity of the stand-up space by infusing global and multicultural perspectives, and thus reshaping comedians’ construction of, and audience engagement with performed jokes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".