Respectfully distrusting ‘Students as Partners’ practice in higher education: applying a Mad politics of partnership
Bibliographic record
Abstract
As a Mad person affiliated with Mad/psychiatric survivor/disability movements, participating in pedagogical partnerships over the past four years has been confusing and ethically fraught. Through engagement with the scholarship on Students as Partners (SaP) practice in higher education, the emerging discipline of Mad(ness) Studies, and my own experiences as a ‘partner’ on various projects, this paper seeks to synthesize a Mad politics of student-staff partnership in the academy. These politics are explored through four themes: (1) Equity? Attention to power dynamics and resulting trauma; (2) Interpersonal concord and consensus? Anger, conflict and collective action; (3) Mutual collaboration? Independence and survivor-led/controlled initiatives; (4) Inclusion? Partnership barriers and possibilities for Mad/disabled students. I end by proposing a politics of respectful distrust as Mad Studies and Mad/disabled people further explore opportunities for coalition-building and alliance with SaP colleagues.
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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 | 0.033 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.032 | 0.094 |
| Scholarly communication | 0.040 | 0.018 |
| Open science | 0.002 | 0.047 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".