Teaching with madness/‘mental illness’ autobiographies in postsecondary education: ethical and epistemological implications
Bibliographic record
Abstract
This paper presents a critical interpretive synthesis of 53 articles describing the pedagogical use of madness/'mental illness' autobiographical narratives in postsecondary education. Focusing on instructor intentions and representations of student learning outcomes, findings indicate that narratives are most commonly used as 'learning material' to engage students in active learning, cultivate students' empathy, complement dominant academic/professional knowledges, illustrate abstract concepts and provide 'real'-life connections to course content. This paper contributes to a conversation across the intellectual traditions of Mad studies, medical humanities, educational research, stigma reduction and service user involvement to interrogate pedagogical uses of autobiographical narratives that remain in uncritical educational terms rather than as a matter of justice for Mad communities. While teaching with narratives will not inevitably result in social justice outcomes, thoughtful engagement with the ethical and epistemological considerations raised throughout this review may increase this possibility by shifting when, why and how we teach with autobiography.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".