Bibliographic record
Abstract
Given that art-making can support self-expression and social integration, there is a growing interest in the research community in developing more accessible artistic computer inputs. However, the conditions needed to achieve mastery of creative processes, particularly for artists with motor impairments, remain to be explored. We look at implementing new adaptive technologies that leverage more natural interactions on the drawing tools, balancing the challenges and skills artists need to navigate their creative stages. We describe the findings of a qualitative first study involving interviews with 15 digital artists with upper limb motor impairments. We analyze the challenges related to artistic workflows, internal and external perceptions, and what disrupts their creative processes. We share a second study where six digital artists with upper limb motor impairments tested an adaptive stylus which captured their pen-based interactions, triggering accessibility features participants thought could improve their artistic workflows. Future work will address the cognitive load introduced by pen gestures, explore strategies to improve detection accuracy to build trust in the technology, and continue emphasizing the value of training personalized models over traditional accessibility features. Our research aims to contribute to designing inclusive technologies by prioritizing the creative aspects of artistic production.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".