Toward Epistemic Justice: A Critically Reflexive Examination of ‘Sanism’ and Implications for Knowledge Generation
Bibliographic record
Abstract
The dominance of medicalized “psy” discourses in the West has marginalized alternative perspectives and analyses of madness, resulting in the under-inclusion (or exclusion) from mainstream discourse of the firsthand experiences and perspectives of those who identify as Mad. We argue that this marginalization of firsthand knowledge(s) demands closer critical scrutiny, particularly through the use of critical reflexivity. This paper draws on Fricker’s concept of epistemic injustice, whereby a person is wronged in his or her capacity as a knower, as a useful framework for interrogating the subjugation of Mad knowledge(s). Also examined is the problem of sanism, a deeply embedded system of discrimination and oppression, as an underlying component of epistemic injustice. Sanism assumes a pathological view of madness, which can be attributed to what Rimke has termed psychocentrism: the notion that pathologies are rooted in the mind and/or body of the individual, rather than the product of social structures, relations, and problems. The paper examines how sanism marginalizes the knowledge(s) of Mad persons and contributes to epistemic injustice, and considers possibilities for advancing social justice using Mad epistemological perspectives
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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 | 0.041 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.017 | 0.166 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.013 |
| 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".