Difference Within and Without: Health Care Providers’ Engagement With Disability Arts
Bibliographic record
Abstract
Re•Vision, an assemblage of multimedia storytelling and arts-based research projects, works creatively and collaboratively with misrepresented communities to advance social well-being, inclusion, and justice. Drawing from videos created by health care providers in disability artist-led workshops, this article investigates the potential of disability arts to disrupt dominant conceptions of disability and invulnerable embodiments, and proliferate new representations of bodymind difference in health care. In exploring, remembering, and developing ideas related to their experiences with and assumptions about embodied difference, providers describe processes of unsettling the mythical norm of human embodiment common in health discourse/practice, coming to know disability in nonmedical ways, and re/discovering embodied differences and vulnerabilities. We argue that art-making produces instances of critical reflection wherein attitudes can shift, and new affective responses to difference can be made. Through self-reflective engagement with disability arts practices, providers come to recognize assumptions underlying health care practices and the vulnerability of their own embodied lives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.034 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".