Stigma, Self-Hatred, and Stereotypes: Using a Critical Disability Studies Framework to Understand Learning Disabilities and Mental Illness
Bibliographic record
Abstract
Critical disability studies (CDS) questions how knowledge is constructed to maintain systems that exclude and control those with disabilities. Without acknowledging the disability experience in conjunction to structural, systematic, and institutional inequalities, we are limiting ourselves to harmful binary thinking. Individuals with disabilities, such as learning disabilities (LDs), are constructed in society as passive, dependent, or failures. Those with mental “illness’” are also stigmatized in similar ways. This paper will ask: In what ways does using a CDS framework, make us think differently about the mental health of individuals with LDs? How can CDS help break the divide between LDs and individuals with a poor mental health and what does this perspective offer to mental health research? There is a clear parallel between the barriers faced by those with LDs and those with mental health hardships. CDS offers a new perspective to disability research by uncovering the social stigma and prejudices faced by those deemed “ill.” This type of research redefines how those with LDs and mental “illnesses” are categorized. CDS can help reduce stigma amongst LDs and mental health, and consequently those suffering from both. For those with LDs, who feel unheard and unworthy, and because of this, have a compromised mental health, a CDS framework can help remove self-blame and self-hatred. Ultimately uncovering how disability reflects a phenomenon to be deconstructed amongst social, political, and systematic barriers.
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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 | 0.032 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.004 |
| Science and technology studies | 0.020 | 0.147 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.008 | 0.011 |
| 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".