Towards a Crip-Ethics: Explorations of Embodied and Embedded Digital Storytelling
Bibliographic record
Abstract
Re•Vision, an assemblage of multimedia story- and arts-based research projects, works creatively and collaboratively to advance social well-being and justice. Drawing from videos made by Canadian healthcare providers in disability-led workshops, we investigate the potential of disability arts to disrupt dominant conceptions of personhood that cast disabled embodiments as inherently vulnerable and dependent against the striving-for-autonomous, invulnerable embodiments of providers, and in so doing, to transform understandings of bodymind difference in healthcare discourses and dynamics. We analyze these videos along three themes: in/vulnerability, compassionate transgressions, and embodied entanglements both fluid and volatile. In exploring ideas of bodymind difference, healthcare providers call into question the normative invulnerable subject that underpins professional standards, revealing its limits in practice, and the importance of their own embodied and embedded experiences as sources of ethical insight. Video themes surface a "crip-ethics" grounded in processual (process-attuned) dimensions of encounters with and across embodied differences. Premised on an affirmative notion of difference, we argue that processual crip-ethics "crips" autonomy by opening to the human-nonhuman relational ensembles that scaffold disabled people's agency toward social justice, and to the non-universalizable that creates new possibilities for health and care in specific instances.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".