DIY in Action: Understanding Do-It-Yourself Practices from Tangible Symbol Cards
Bibliographic record
Abstract
Do-It-Yourself (DIY) assistive technology can be better suited to the needs of people with disabilities than commercial ‘off-the-shelf’ devices. Yet, DIY practices conventionally involve barriers to entry that can dissuade or intimidate practitioners. Tangible symbols (TS) are low-tech augmentative and alternative communication (AAC) products with a history of practitioners working alongside end-users to DIY. In this paper, we study the enthusiasm and practice of DIY with TS. We present results from an initial investigation into DIY practices by practitioners that use TS. We analyze survey data collected by Brady et al. (2025) from 107 respondents, and 73 self-reported customizing their TS products to meet the needs and contexts of the individuals they serve. We share the survey self-reported motivations for engaging in DIY activities. We find that this data offers evidence that there is sustained enthusiasm in DIY with TS by practitioners. We reflect on these findings and draw connections between DIY and assistive technology abandonment, and how commercial products can be designed to support DIY practice. The use of DIY by practitioners signals both a desire to further integrate DIY into their profession, and an interest in future assistive technology products that employ a similar DIY-centered design framework.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".