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Record W2760694503 · doi:10.1353/book76826

Funny How?: Sketch Comedy and the Art of Humor

2020· book· en· W2760694503 on OpenAlexaboutno aff
Alex Clayton

Bibliographic record

VenueState University of New York Press eBooks · 2020
Typebook
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsnot available
Fundersnot available
KeywordsSketchComedyArtVisual artsComputer scienceAlgorithm

Abstract

fetched live from OpenAlex

What makes something funny? This book shows how humor can be analyzed without killing the joke. Alex Clayton argues that the brevity of a sketch or skit and its typical rejection of narrative development make it comedy-concentrate, providing a rich field for exploring how humor works. Focusing on a dozen or so skits and scenes, Clayton shows precisely how sketch comedy appeals to the funny bone and engages our philosophical imagination. He suggests that since humor is about persuading an audience to laugh, it can be understood as a form of rhetoric. Through vivid, highly readable analyses of individual sketches, Clayton illustrates that Aristotle’s three forms of appeal: logos, the appeal to reason; ethos, the appeal to communality; and pathos, the appeal to emotion, can form the basis for illuminating the inner workings of comedy. Drawing on both popular and lesser-known examples from the United States, United Kingdom, and elsewhere—Monty Python’s Flying Circus, Key and Peele, Saturday Night Live, Airplane!, and Smack the Pony—Clayton reveals the techniques and resonances of humor. “This book tackles head on the assumption that to examine comedy is to destroy it. Clayton isn’t out to make the reader laugh all over again at these sketches and extended comic riffs. His point is that comedy, like any other kind of artistic performance, should be amenable to aesthetic redescription by an attentive critic. I know of no other book that contends with the assumptions and claims of comedy theories in the way this one does. There is nothing else like it out there.” — Brenda Austin-Smith, coeditor of The Gendered Screen: Canadian Women Filmmakers

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.009
Scholarly communication0.0080.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.002

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 head0.061
GPT teacher head0.240
Teacher spread0.179 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2020
Admission routes1
Has abstractyes

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