Illustrations, Fiction, and the Autobiographical: A Writerly Reading of That Scatterbrain Booky
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".