Understanding How People with Limited Mobility Use Multi-Modal Input
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
People with limited mobility often use multiple devices when interacting with computing systems, but little is known about the impact these multi-modal configurations have on daily computing use. A deeper understanding of the practices, preferences, obstacles, and workarounds associated with accessible multi-modal input can uncover opportunities to create more accessible computer applications and hardware. We explored how people with limited mobility use multi-modality through a three-part investigation grounded in the context of video games. First, we surveyed 43 people to learn about their preferred devices and configurations. Next, we conducted semi-structured interviews with 14 participants to understand their experiences and challenges with using, configuring, and discovering input setups. Lastly, we performed a systematic review of 74 YouTube videos to illustrate and categorize input setups and adaptations in-situ. We conclude with a discussion on how our findings can inform future accessibility research for current and emerging computing technologies.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it