Beyond Psycho-Emotional Disablism: Reframing Learning Disabilities as Resources in the Lives of Post-Secondary Students
Bibliographic record
Abstract
While much research on learning disabilities (LDs) (e.g., dyslexia, dyscalculia, ADHD) focuses on academic under-achievement, there is a need to move beyond deficit-based models of disability and explore its psycho-emotional dimensions. To fill this gap, this study examines the lived experiences of post-secondary students with LDs, focusing on their emotional well-being, social relationships, and resilience, through a social-relational framework. Using qualitative interviews and journal reflections, we ask: (1) To what extent do post-secondary students with LDs experience psycho-emotional disablism? (2) What mechanisms contribute to their positive emotional and social well-being? (3) In what ways do students with LDs perceive their disability as a resource? Emerging themes highlight the dual nature of students’ experiences—while they report psycho-emotional disablism (e.g., bullying, stigma, internalized ableism, mental health challenges), they also describe resilience, heightened empathy, and adaptive coping strategies. These findings underscore the need to acknowledge both the barriers and strengths associated with LDs and other neurodiversities. The study emphasizes the importance of directly engaging individuals with LDs to understand their psycho-emotional experiences, including viewing their disabilities as sources of personal growth, strength, and resilience. Considering LDs as a resource, rather than a limitation, could have significant implications for individuals and educational frameworks.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".