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Record W2527814371 · doi:10.22230/src.2016v7n1a267

Taking Parodies Seriously: A Review of Pierre Huard’s La parodie dans la bande dessinée franco-belge (“Parody in French and Belgian Comics”)

2016· review· en· W2527814371 on OpenAlexaffvenue
Éric Spalding

Bibliographic record

VenueScholarly and Research Communication · 2016
Typereview
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsComicsArtLiteratureArt history

Abstract

fetched live from OpenAlex

This article presents and evaluates La parodie dans la bande dessinée franco-belge (Parody in French and Belgian Comics) by Pierre Huard. In this book, the author is interested in examining how parody works and how it evolved in French and Belgian comic books in the second half of the twentieth century. To accomplish this goal, Huard presents an in-depth semiotic analysis of excerpts from ten parodic comic books published in Belgium or France between 1952 and 1994. He shows precisely what the creators of the comic books amplify and modify relative to the genres that they are parodying. He also discusses a tendency in the late 1970s and 1980s towards parodying comic books themselves in addition to parodying novels, movies and TV series.

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)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.008
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.107
GPT teacher head0.386
Teacher spread0.279 · 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
GenreReview

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
Published2016
Admission routes2
Has abstractyes

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