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Record W4411128298 · doi:10.18061/dsq.v44i3.8256

Towards a Crip-Ethics: Explorations of Embodied and Embedded Digital Storytelling

2025· article· en· W4411128298 on OpenAlexaboutno aff
Nadine Changfoot, Carla Rice, Evadne Kelly, Luka Stojanovic

Bibliographic record

VenueDisability Studies Quarterly · 2025
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsEmbodied cognitionStorytellingDigital storytellingSociologyArtComputer scienceNarrativeArtificial intelligenceLiteraturePedagogy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.127
GPT teacher head0.454
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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