From Stigma to Strength: Understanding Psycho-Emotional Disablism and the Transformative Potential of Learning Disabilities
Bibliographic record
Abstract
This paper argues for reconceptualizing learning disabilities (LDs) as a natural part of human variation rather than a deficit, advancing the conversation about LDs within Critical Disability Studies (CDS) and the social-relational model (SRM). While much critical scholarship has focused on defining LDs and addressing their structural (e.g., educational barriers, access to resources) and neurological (e.g., cognitive processing differences) factors, less attention has been paid to the psychological impact of disablism. This paper addresses this gap by exploring the concept of psycho-emotional disablism, which highlights how societal attitudes and ableist structures create internalized stigma, anxiety, and emotional challenges for individuals with LDs. However, the most instrumental piece of this research is understanding how disability can be understood as part of what it means to be human. Drawing on positive psychology and the neurodiversity paradigm, this paper argues for the transformative potential of recognizing the strengths and perspectives associated with LDs, including innovation, adaptability, and nonlinear thinking. By situating LDs within a broader framework of diversity, this research aims to challenge deficit-oriented views of disability and advocate for systemic and relational approaches that promote well-being and empowerment. Keywords: Learning disabilities, psycho-emotional disability, critical disability studies, social-relational, strengths-based framework.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.108 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".