‘It’s complicated’: blending disability and mad studies in the corporatising university
Bibliographic record
Abstract
Introduction Let's say you accept the principle that all study of past cultural practice is a contribution to the history of the present. And let's say that you also accept the testimony of your exhausted body that the present is a time of struggle. (Marc Bousquet, 1998) Across their diversity, the contributors to this book address the question of whether and how ‘mental distress’ and disability do/might come together in theory, policy and/or practice. I am pleased to be included in the collection although, at one point, I thought I might have to deliver my text in short hits of email. It was a wry nod at what has been happening to me since I became director of the School of Disability Studies at Ryerson University, Toronto. After just three years, the enforced discipline of providing brief, objective and functional communications – pressed by relentless reporting deadlines and compressed into unyielding electronic templates – has eroded my ability to think and write in more fulsome ways; to complicate rather than (over) simplify; to make problematic rather than make nice (Smith, 1987). This chapter is a chance to fight back. In that spirit, I draw eagerly from the politicised word play of the psychiatric survivor activists who surround and inform me: mad; mad-identified; mad-positive. The shock of these terms make you STOP…and say… ‘WHAT?’ And in that interruption, that space of sudden confusion, we can invoke a strand of human experience and history that pre-dates and challenges psychiatric dominance. We can seize an opening into ‘something otherwise’ – something we could call Mad Studies. My task is to think about how madness became an identifiable and substantive part of the curriculum, instructional and student life in disability studies at Ryerson. Within our programme, how or where does madness fit in relation to disability? How do the two exist within the faculty/staff team of the programme itself? Are there tensions in the relationship? And can we talk about them? In the absence of much literature on this topic, I will draw from practice in the school (Miettinen et al, 2009), keenly aware that the broader context for my description is the long downsizing of education in western capitalist nations (Leitch, 2005).
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.038 | 0.064 |
| Scholarly communication | 0.028 | 0.016 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".