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Record W2517760406 · doi:10.1177/0160597615621594

Aesthetic as Analysis

2015· article· en· W2517760406 on OpenAlexaboutno aff
Shawn Chandler Bingham, Sara E. Green

Bibliographic record

VenueHumanity & Society · 2015
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCONTESTComedyContext (archaeology)PsychologySociologyTragedy (event)Social psychologyGender studiesAestheticsSocial scienceLiteratureLawPolitical scienceHistory

Abstract

fetched live from OpenAlex

A wide range of comedians with disabilities has recently been reclaiming the comedy stage as a space in which to contest inequality. The work of disabled comedians highlights the utility of humor as an alternative lens into social life, especially the complexity of the disability experience. Despite the rise of disability humor as a form of activism, scholars have identified disability humor as an undertheorized area. Drawing on in-depth interviews with 10 professional comedians from Canada, the United Kingdom, and the United States, we examine common conceptual ground between humor theory and disability theory—focusing on how humor can function as an epistemological and critical lens for viewing disability in everyday social context. Our analysis suggests that even types of humor that have traditionally been used to demean and disable can be (and are) wielded by people with disabilities, on and off the stage, as both a shield and a sword with which to contest the constraints imposed by an ableist world, while also countering the widespread belief that disability is only and always a personal tragedy.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0070.027
Scholarly communication0.0130.006
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0340.004

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 head0.098
GPT teacher head0.387
Teacher spread0.289 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations17
Published2015
Admission routes1
Has abstractyes

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