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Record W429038484

Graphic Drama: Reading Shakespeare in the Comics Medium

2014· article· en· W429038484 on OpenAlexfundno aff
Russell Hugh McConnell

Bibliographic record

VenueScholarship@Western (Western University) · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsComicsDramaReading (process)ArtLiteratureVisual artsLinguisticsPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

This project adopts a formalist method of literary analysis to approach the modern genre of Shakespearean comic book adaptations. These texts have as yet received little attention from the academy, despite their sophisticated engagement with problems of visualizing the transition from stage to comics page, as well as their capacity for making original contributions to the interpretation of Shakespearean drama. The formalist method that this thesis employs is derived from the foundational work of comics theorists Will Eisner and Scott McCloud, combined with an understanding of Shakespearean language and stage conventions. Once this method is developed and explained, the dissertation uses it in a series of readings demonstrating the ability of Shakespearean comic books to emulate and elaborate on early modern staging practices, engage sensitively and imaginatively with Shakespeare’ literary language, and actually contribute to interpretive scholarly discussion of Shakespearean drama. Although many texts are examined, there is a particular emphasis on versions of The Merchant of Venice and Macbeth, which represent two extremes of the comics medium’s versatile formal engagement with Shakespearean themes.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.096
GPT teacher head0.282
Teacher spread0.186 · 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 designObservational
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
Published2014
Admission routes1
Has abstractyes

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