Potentials of Digital Assistive Technology and Special Education in Kenya
Bibliographic record
Abstract
Technology specifically designed for people with disabilities is important in lowering boundaries to education, employment and basic life needs. However, the growth of a vibrant tech sector in Kenya has had little effect on the prevalence of digital assistive technology in the country. In this chapter, the authors report on initial explorations undertaken in Kisumu, Kenya to identify existing strengths, relationships, and gaps in access to digital assistive technology. The goal was to explore opportunities for initiatives in participatory design of assistive technology, using an international community/academic partnership. Relevant literature and projects from the areas of Information and Computer Technology for Development (ICT4D), Human-Computer Interaction (HCI) and Critical Disability Studies are reviewed and, these theories are grounded in the authors' experience working with stakeholders in the region. The conclusion discusses promising future directions for participatory and collaborative research in Kenya, and more broadly in the East African context.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".