Reimagining Learning Disabilities: Embracing Difference as an Empowering Resource Amidst Psycho-Emotional Disablism
Bibliographic record
Abstract
This dissertation explores the experiences of psycho-emotional disablism in individuals with learning disabilities (LDs) and examines how, despite these challenges, they can reframe their LD as a resource. Employing a Critical Disability Studies (CDS) and a social-relational framework, three interconnected studies were conducted. The first study analyzed the public narratives of three prominent LD advocates—Richard Branson, Henry Winkler, and Barbara Corcoran—highlighting how they view their LDs as unique strengths. The second study focused on disability memoirs, revealing both the disabling and empowering experiences of individuals with LDs, highlighting their distinctive strengths. The third study involved qualitative analyses through individual interviews and journal entries with post-secondary LD students, providing a comprehensive understanding of both the challenges and empowering aspects of their lived experiences. These discussions underscore one of the greatest strengths of this dissertation: its ability to integrate positive psychology with disability studies to explore how individuals with LDs, within the broader context of social structures, can navigate psycho-emotional disablism and reconceptualize disability as a resource. The research emphasizes the importance of understanding disability within broader socio-cultural contexts and recognizing the potential for growth, innovation, and success inherent in neurodiversity. It calls for a paradigm shift in education and support systems to promote inclusivity and equity. By highlighting the voices and experiences of individuals with LDs, this dissertation contributes to the broader discourse on disability, empowerment, and self-advocacy, advocating for a more nuanced and strengths-based understanding of LDs. Keywords: learning disabilities, critical disability studies, social-relational, psycho- emotional disablism, strengths-based, resource
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.064 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".