"Knowledge Comes Through Participation": Understanding Disability through the Lens of DIY Assistive Technology in Western Kenya
Bibliographic record
Abstract
People with disabilities in Low- and Middle-Income Countries (LMICs) have limited access to digital assistive technologies (ATs). Most ATs in LMICs are manufactured elsewhere and are expensive and difficult to maintain. Do-It-Yourself Assistive Technologies (DIY-ATs) designed, customized, and repaired by non-technical users offer exciting directions in these contexts. We have been exploring the possibilities and challenges of DIY-ATs in Western Kenya, using community-engaged workshops in rural and urban special education schools for the past three years. We present findings from a concluding-stage research activity: a multiple stakeholder focus group where teachers, disability advocates, and representatives from the local government and technology innovation hubs, discussed the possibilities and challenges of addressing disability issues through DIY-ATs in this context. Participants identified opportunities for DIY-ATs for social inclusion, disability assessment, and inclusive education, and shared concerns about their sustainability, safety, and contextual relevance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.032 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".