What It Is to Perform , Not Tell , Jokes: Toward a Manifesto of Stand-Up Research
Bibliographic record
Abstract
Abstract: This essay makes a case for studying stand-up comedy with theatre analytical tools by highlighting its performative nature and discussing how body interactivities within its enactment produce meaning and humor beyond what comedians say. It provides an alternate reading of stand-up comedy to the prevalence of linguistic evaluations, which often conflate stand-up art with other comedic traditions that are not performed. I argue that such perspectives often downplay the co-participation of the audience and what comedians do with their bodies. Citing joke samples from three African diasporic comedians—Gina Yashere, Urzila Carlson, and Hoodo Hersi performing in the US, New Zealand, and Canada, respectively—I explore the use of the body, audience involvement, and other performed aspects from which hilarity is derived.
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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 | 0.041 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.051 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| 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".