Crip Digital Intimacies: The Social Dynamics of Creating Access through Digital Technology
Bibliographic record
Abstract
Disabled people are uniquely positioned in relation to the digital turn. Academic ableism, the inaccessibility of digital space, and gaps in digital literacy present barriers, while, at the same time, disabled, Deaf, and neurodivergent people’s access knowledge is at the forefront of innovations in culture and crip technoscience. This article explores disability, technology, and access through the concept of crip digital intimacy, a term that describes the relational and affective advances that disabled people make within digital space and through digital technology toward accessing the arts. We consider how moments of crip digital intimacy emerged through Accessing the Arts: Centring Disability Perspectives in Access Initiatives—a research project that explored how to make the arts more accessible through engaging disabled artist-participants in virtual storytelling, knowledge sharing, and art-making activities. Our analysis tracks how crip digital intimacies emerged through the ways participants collectively organized and facilitated access for themselves and each other. Guided by affordance theory and in line with the political thrust of crip technoscience, crip legibility, and access intimacy, we argue that crip digital intimacy emphasizes the interdependent and relational nature of access, recognizes the creativity and vitality of nonnormative bodyminds, and understands disability as a political—and frequently transgressive—way of being in the world.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.021 |
| 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".