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Record W2227639400

Illustrations, Fiction, and the Autobiographical: A Writerly Reading of That Scatterbrain Booky

2007· article· en· W2227639400 on OpenAlexaboutno aff
Andrea Schwenke Wyile

Bibliographic record

VenueCanadian Children's Literature / Littérature canadienne pour la jeunesse · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)HumanitiesMeaning (existential)Character (mathematics)ArtArt historyBiographyLiteraturePhilosophyLinguisticsEpistemology
DOInot available

Abstract

fetched live from OpenAlex

Resume: Cet article puise d'un travail signe d'Eileen Conway et publie dans un numero precedent de la Canadian Children's Literature concernant l'effet des images paraissant dans le roman de Bernice Thurman Hunter, That Scatterbrain Booky . Il examine le role de ces images dans l'effet reciproque entre le cote fictif et le cote autobiographique, selon le sens du « texte scriptible » de Roland Barthes, et demontre que ces images creent un metalepse qui fait remarquer les points communs entre la protagoniste-narratrice et l'auteure ainsi qu'entre les evenements fictifs et historiques. Summary: This paper takes up ideas explored by Eileen Conway in a past issue of Canadian Children's Literature regarding the effect of the illustrations in Bernice Thurman Hunter's That Scatterbrain Booky and examines the interplay between fiction and autobiography in terms of Roland Barthes's idea of writerly reading. It argues that the pictures contribute to the metalepses in the text that both destabilize it and enable a writerly reading of it by drawing attention to the overlaps between character-narrator and author, between fictional events and historical events or artifacts, and thus to the story's connections to the real world. The concept of a writerly reading is used to try to untangle the relationships between form, content, and meaning.

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 categoriesMeta-epidemiology (narrow)
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 score0.911
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.006
GPT teacher head0.183
Teacher spread0.177 · 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 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

Citations0
Published2007
Admission routes1
Has abstractyes

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