Out of Place, Out of Mind: Min(d)ing Race in Mad Studies Through a Metaphor of Spatiality
Bibliographic record
Abstract
This article examines the racial politics of Mad Studies in Canada through a metaphor of spatiality, underscoring the urgency of an antiracist Mad Studies paradigm. Drawing on critical race scholarship which situates “madness” as reliant on and informed by white supremacist and colonial logics of rationality and reason (Bruce 2017), I foreground claims made by critical race scholars of racialized madness as contingent on and informed by histories of slavery, genocide, and everyday realities of racism and racial violence which an anti-racist Mad Studies project must contend with. By locating the racialization of Mad Studies within a metaphor of spatiality, I heuristically problematize the “space” available for racialized subjects to re/claim madness within contemporary Mad Studies paradigms. I conclude that in failing to rigorously unpack the relations of race which undergird understandings of madness, and to challenge the presence of white supremacy in the Mad Studies discipline, scholars potentially perpetuate a colonial project of “othering” and consequentially maintain the systems of psychiatric violence they seek to undo. Centralizing race in Mad Studies exposes the workings of white supremacy in logics of violence against Mad people more broadly and is thus necessary to an anti-racist and anti- oppressive Mad Studies project.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.071 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".