Mad, Maddened, and Maddening: A Mad Duoethnographic Exploration of Undergraduate Education
Bibliographic record
Abstract
Higher education is known to be a hostile environment towards mad(dened) and disabled students, faculty, and staff, who experience high amounts of discrimination, exclusion, and epistemic and institutional violence (Shanouda, 2019). As two activist/scholars with experiences teaching and learning within mad(dening) higher education conditions, we are committed to activism and critique in higher education that cultivates affirming conditions for mad(dened) students through disruptions of the ableist and sanist status quo in higher education. Through a duoethnographic approach (Sawyer & Norris, 2012) that emphasizes the tenets of Pinar’s (1994) currere—which involves an autobiographical exploration of one’s personal experiences within education—this article explores three themes pertaining to undergraduate education: (a) Mad identity, (b) maddened subjectivities, and (c) maddening higher education. We explore these themes through personal and intimate co-writing, with a specific focus on our encounters and learnings with and from mad community, the currently maddened state of neoliberal higher education, and our desire to promote a political maddening of higher education. While distilling these themes, we also advocate for their interconnections and weave our life histories teaching and learning in undergraduate education specifically. Throughout our duoethnographic writing, we strive to critique and disrupt the currently exclusionary conditions of undergraduate education while politicizing madness as an identity and coalitional community. We end by providing recommendations for how higher education can create more affirming learning conditions for undergraduate students while creating space mad community and activism.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.030 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".