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Record W4234960622 · doi:10.1080/21504857.2015.1122651

Frida<sup>2</sup>

2016· article· en· W4234960622 on OpenAlexaff
Natalie B. Pendergast

Bibliographic record

VenueJournal of Graphic Novels & Comics · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComicsPortraitStorytellingNarrativeLiteratureArtPaintingIntertextualityCharacter (mathematics)ReflexivityIconArt historyVisual artsSociologyComputer scienceAnthropology

Abstract

fetched live from OpenAlex

In both Marco Corona’s and Gilbert Hernandez’s comic-book biographies of Frida Kahlo, they adapt several of her self-portrait paintings into cartoon caricatures, using her art as remade, drawn visual intertext to tell her story in a new form. Along with stylistic and compositional alterations that reshape her narrative in these comic books, Corona and Hernandez also accentuate the generic shift of selections of her oeuvre from autobiographical to biographical images. This article explores the implications of this shift from a self-reflexive genre to an other-focused one. As I show, the works of Corona and Hernandez use the formal elements of the comic book to emphasise – to varying degrees – the neutralising characteristic of intertextuality and the challenges of representing a cultural icon such as Frida Kahlo. Indeed, not only do their comics tell Frida Kahlo’s story, they also critically engage with her self-portraits as visual and autobiographical devices. The three main strategies Corona and Hernandez employ to do this include blending the identities of Kahlo the artist and Kahlo the character in intertext; using frames as seams to stitch or reconfigure her body; and, frequently referencing different pop cultural icons to add significance to their narratives. Ultimately, my analyses aim to show that these comic books make explicit the power of this medium to interweave several dimensions of life storytelling.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.221
Teacher spread0.195 · 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 teacher head, 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

Citations1
Published2016
Admission routes1
Has abstractyes

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