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Record W809697800

LAUGHING IN CIRCLES: EXPLORING THE RELATIONSHIP BETWEEN POLITICALLY CORRECT DISCOURSES AND STAND-UP COMEDY IN TORONTO

2012· dissertation· en· W809697800 on OpenAlexfundaboutno aff
Meghna George

Bibliographic record

VenueMacSphere (McMaster University) · 2012
Typedissertation
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
FundersMcMaster University
KeywordsComedyGender studiesSociologyAestheticsMedia studiesArtLiterature
DOInot available

Abstract

fetched live from OpenAlex

It has been suggested that Canadian society increasingly promotes a rhetoric of tolerance, through the dissemination of multicultural and politically correct discourses. At the same time, there has been a growth in the popularity of performances that seemingly counters this national image; that of risqué stand-up comedy. This dissertation explores if an institutionalized rhetoric of multiculturalism and “PC”, popularized since the late 1980s, is pierced, protracted and parodied within risqué stand-up comedy while remaining confined within spatial and temporal boundaries. Furthermore, this relationship between comedy and multicultural and “PC” discourses illuminates the nature of power circulating within our dialogues about issues of discrimination in contemporary Canadian society. This thesis establishes that both stand-up comic performances and politically correct rhetoric share a carnivalesque nature that while degrades authorial discourses, is ultimately constrained within its self-definition.

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)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.959

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.005
Science and technology studies0.0480.036
Scholarly communication0.0180.005
Open science0.0030.011
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.087
GPT teacher head0.287
Teacher spread0.200 · 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 designQualitative
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

Citations0
Published2012
Admission routes2
Has abstractyes

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