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Record W4378717134 · doi:10.1007/s10583-023-09529-9

A School Story, Not a Student Story: The Dyslexic Diagnosis Paradigm in Children’s and Young Adult Literature

2023· article· en· W4378717134 on OpenAlexfundno aff
Elizabeth Leung

Bibliographic record

VenueChildren s Literature in Education · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsShameNarrativeDyslexiaPsychologyDeclarationStatus quoClosure (psychology)ScholarshipDisability studiesEmpathySocial psychologyDevelopmental psychologyReading (process)SociologyLinguisticsGender studiesPolitical scienceLaw

Abstract

fetched live from OpenAlex

Representations of dyslexia have a history of educational and literary scholarship primarily concerned with how dynamic characters with learning disabilities are and if they are positively portrayed. This article uses narrative theory to analyze how diagnosis operates on a structural level to create what I call the dyslexic diagnosis paradigm. Examining school stories featuring characters with dyslexia published between 2007 and 2020, I demonstrate how this paradigm functions through a structural closure of struggle, diagnosis and accommodations, and a psychological closure consisting of shame, declaration, and acceptance within these novels. Variations or polytypes of this narrative are also common within this corpus which maintain the psychological closure of shame, declaration, and acceptance present within the prototypical narrative. While some disability counternarratives or dyslexic persistence narratives nuance the school story, the dyslexic diagnosis paradigm ultimately remains prevalent and upholds the medical model of disability within the educational system, promoting the flawed status quo of disability rather than asking readers to question the validity of the systems which enforce them.

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), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
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 studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.246
Teacher spread0.239 · 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 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

Citations4
Published2023
Admission routes1
Has abstractyes

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