Episodic disability in the neoliberal university: Stories from the Canadian context
Bibliographic record
Abstract
Abstract This article explores how episodic disability (ED) is created and intensified by, present within, and pushed out of the neoliberalized academy in Canada. ED is an umbrella term for a range of physical, mental, and neurological conditions characterized by fluctuation and unpredictability. Over two million working‐age Canadians are affected by ED, with women more likely to be impacted. To consider how ED interacts with post‐secondary education, we put feminist disability theories of embodied precarity, crip time, and disability justice into conversation with multimedia stories created by post‐secondary women workers with EDs, with story‐makers contextualizing and theorizing their creations, and revealing their complex embodied and embedded experiences. We chose these from stories generated in two research projects focused on transforming negative concepts of disability to serve as an “archivy” of embodied precarity that challenges ED's erasure in the academy. We think with the stories to analyze power and resistance in and on gendered and raced academic bodies along three overlapping themes: debility and vulnerability in the neoliberal university; fault‐lines of neoliberal time and embodied time; and EDs as produced in, and pushed out of, the neoliberal academy. Considering interrelations between ED and post‐secondary education under neoliberalism, we argue that feminist disability theory and justice praxis challenge debilitating and exclusionary expectations that harm people with EDs in and outside of the academy.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.068 | 0.029 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".