I tried everything. Nothing works: Challenges and Creative Processes from Digital Artists with Upper Limb Motor Impairments
Bibliographic record
Abstract
Digital artists with motor impairments in their upper limbs face considerable barriers to accessibility when using drawing tools. Our work aims to investigate the complex relationship between digital artists’ creative processes and their accessibility challenges. We conducted 15 interviews with artists who use input devices to make digital art, analyzing their accessibility challenges for producing digital artwork. We reviewed how effective the solutions are in diminishing the impact on their creative processes and identifying design opportunities for the research community. Using thematic analysis, we look at the challenges participants reported in their artistic production, including managing pain, discomfort, and injuries alongside workarounds. Secondly, the artists reported the complexities of managing internal and external perceptions. Lastly, the ways creative processes are impacted by the accessibility challenges and solutions related to their upper limb motor impairments. We discuss research directions which can better address the impact of accessibility challenges on creative processes, the balance of creative agency over tools, and design insights for more accessible artistic technologies.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".