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Record W4313396243 · doi:10.1080/21504857.2022.2152068

Laurie Halse Andersen’s <i>speak: the graphic novel</i> : creating and resisting aesthetic distancing to discuss the trauma of sexual violence

2022· article· en· W4313396243 on OpenAlexafffund
Joti Bilkhu

Bibliographic record

VenueJournal of Graphic Novels & Comics · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsYork University
FundersYork University
KeywordsDistancingPsychoanalysisPsychologySexual violenceAestheticsArtCoronavirus disease 2019 (COVID-19)CriminologyMedicine

Abstract

fetched live from OpenAlex

In 2018, Laurie Halse Andersen’s groundbreaking novel Speak (1999) was adapted into a graphic novel, a form that effectively combines visual and verbal storytelling techniques. In stories about sexual assault, it is common for authors and illustrators to employ aesthetic distancing in children’s and YA literature. Aesthetic distancing includes the use of both visual and verbal techniques to provide a sense of what Jacqueline F. Eastman calls ‘controlled danger’ where the narrative becomes ‘comfortably exciting rather than overwhelming’ (75). By drawing on art and visual design theory and principles, this article will analyse where Speak: The Graphic Novel (2018) uses aesthetic distancing, but more importantly, where it is rejected as a mode of representation. I suggest that Andersen and the illustrator Emily Carroll take a middle ground approach, by which I refer to the fact that although aesthetic distancing is used, the narrative still presents threatening text and images to demonstrate that healing from sexual violence is an on-going process. Specifically, I analyse the jaggedness of the artistic style, vague pronoun usage and language, power relations, intertextuality, and the symbol of the mouth. I conclude by considering the ethical ramifications of employing aesthetic distancing in stories about sexual assault.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.222
Teacher spread0.198 · 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.

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

Citations1
Published2022
Admission routes2
Has abstractyes

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