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Record W4280534851 · doi:10.1080/21565503.2022.2071304

Gender, morality and violence in anthropomorphic metaphors depicted in Canadian political humor

2022· article· en· W4280534851 on OpenAlexafffundabout
Rissa Reist

Bibliographic record

VenuePolitics Groups and Identities · 2022
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFraming (construction)PoliticsCitizenshipGender studiesSociologyIdeologyFemininityMasculinityMoralityConversationPolitical scienceLaw

Abstract

fetched live from OpenAlex

This article considers the historical and ongoing use of gendered anthropomorphic metaphors in Canadian political humor. It asks how national and sub-national identities have been articulated through gendered bodies in Canadian political humor and what types of underlying cultural and ideological assumptions about citizenship are expressed in these metaphors? Two case studies of Canadian political humor were conducted and analysed through the lens of feminist critical discourse analysis. The findings reveal a tendency for political humor to use anthropomorphic metaphors to enforce cultural understandings of acceptable and unacceptable forms of citizenship. These discussions are highly gendered and often exist in conversation with intersectional issues such as race and class. Overall, these metaphors enforce the acceptability of white masculinity in Canadian social, political, and cultural rhetoric while framing femininity as a precursor to undesirable forms of citizenship.

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)0.008
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 score0.132
Threshold uncertainty score0.413

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.004
Science and technology studies0.0330.058
Scholarly communication0.0100.004
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.036
GPT teacher head0.323
Teacher spread0.287 · 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
Published2022
Admission routes3
Has abstractyes

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