Mad, mentally ill and neurodivergent professionals: epistemic injustice in practice
Bibliographic record
Abstract
In addition to fields of practice, the professions lay claim to particular domains of knowledge and expertise, privileging specific ways of thinking and doing. This limits space within the professions for the strengths of those who identify as Mad, mentally ill and/or neurodivergent (MMIND). This paper explores the experiences of twelve professionals from across Canada (occupational therapy, nursing, medicine, social work and academia) who identify as MMIND. Reflexive thematic analysis of qualitative interview data reveals epistemic injustice and violence – injustice concerning credibility as a legitimate knower. MMIND professionals were cast as incapable, incompetent, incredible, unwell, and in need of care, eroding their assumed authority and expertise as professionals. Though participants saw their MMIND identities as beneficial to their work, they navigated disclosures with considerable care. Pressured to contort their work and their self-presentation to meet normative standards, they experienced the epistemic violence of ‘smothering’ their own narratives to maintain credibility.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.074 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 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".