The Politics of Passing: Disabled and Mad Students’ Experiences of Disclosure in Higher Education
Bibliographic record
Abstract
This dissertation explores disabled and mad students experiences of disclosure in higher education from a Deleuzian new materialist perspective. This theoretical orientation conceptualized students’ movements through the university as rhizomatic, and included passing, moving into the middle, and coming out. The author argues that these movements create an affective politics within the university that can change and rearrange the university in meaningful ways for individuals with embodied differences. The author organizes the relationship between students and the university using la paperson’s (2017) idea that there are two competing machinic-desires operating within higher education. In this thesis, these desires are defined as eugenic and crip. Eugenic desires operate within the university and construct assemblages with eugenic ends and are operationalized by the disclosure process. Crip desires, on the other hand are a passional understanding of disability and madness as multiplicity (rather than a binary) that can build the university anew. Crip desires are constituted by disabled and mad students and create assemblages that deterritorialize the university. The impact of disclosure was made clear by the 17 disabled and mad participants who shared stories of disclosure’s affective violence (when registering as a “student with a disability,” or disclosing to faculty or staff, and petitioning a grade or a course). The author argues that disclosure produces violence in higher education because it reduces embodied differences to biomedical and psychiatric explanations – often limiting disability/madness from becoming anything other. However, there are other ways that students speak about disability and move in-between identities that allow for a different understanding of disability and madness to emerge. When students come out-crip, they story difference rather than disclose disability, and therefore, create new conditions for how access might be arranged. The author argues that a more complex understanding of students’ movements and actions as rhizomatic reveals that they are engaged in micropolitical work with identity that can create small and slow cracks in the university. In asking participants to imagine a different university, this project engages in a thinking-resistance that works towards actualizing different conditions for learning in higher education.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.039 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".