In Their Own Words: The Voices of Disabled First-Person Protagonists in Children's and Young Adult Literature
Bibliographic record
Abstract
Located in the field of education, my dissertation examines the representations of disability in 12 popular children’s and young adult novels that are commonly being taught in Ontario schools. Throughout my study, I have used autocritical disability studies, autoethnography, and critical inquiry-based research methods to answer the central research questions of my dissertation within a critical disability studies informed social constructivist theoretical frame. 1. How are disabled first-person protagonists depicted in the contemporary realistic children’s and young adult literature that is being taught in grades 7 through 12 in Ontario schools? 2. How can “asset-based pedagogies” (Waitoller & King Thorius, 2023, p. xv) be used by teachers in the language arts classroom to teach disability-themed children’s and young adult literature in a disability “culturally authentic” (Brown, 2020, p. 141) manner? Based on Bates’ (2017) survey of the texts being taught in grades 7-12 in Ontario schools, I initially located 39 disability-themed children’s and young adult novels which I reduced to the 12 texts containing 12 disabled first-person protagonists that I have analysed in this study, using a comprehensive inclusion and exclusion criteria. I thereafter applied criteria informed by critical disability studies, education, and English literature scholars to critique disability representation in cultural texts. By deconstructing the portrayals of disability in 12 popular novels through a critical disability studies lens, I have exposed the systemic ableism that is present in many of the stories containing disabled characters which negatively impacts students’ beliefs about disability and disabled people. In response to my second research question, I have developed a new application regarding culturally relevant and responsive pedagogy (CRRP) by demonstrating how CRRP can be used to teach disability-themed literature in a culturally affirming manner in the language arts classroom by using the middle-grade novel A Kind of Spark (McNicoll, 2020) as an illustration. My thesis concludes by making recommendations for teacher preparation programs and in-service teachers for future research in literary disability studies, and disability studies in education regarding the selection, analysis, and teaching of disability-themed children’s and young adult literature.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.029 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".