Rethinking digital and AI inclusion: participatory and intersectionality-informed methods for disability and migrant justice
Bibliographic record
Abstract
Everyday consumer technologies are increasingly integral to autonomy, mobility, and social participation among people with disabilities and migrants from culturally and linguistically diverse (CaLD) backgrounds. However, these technologies often remain inaccessible and exclusionary at the intersection of these identities. This study examined how CaLD migrants with disabilities engage with everyday consumer technologies using participatory and intersectionality-informed approaches. This article focuses on Stage Two of the Autonomy, Diversity & Disability: Everyday Practices of Technology project, funded by the Australian Research Council industry partnership grant (LP: 190900099), which involved individual interviews, creative workshops, guided discussions, post-workshop reflections, and the co-creation of AI-generated e-books. Drawing on three case studies, the analysis identified three key findings: (1) participants experienced a disproportionate burden in navigating digital accessibility and advocating for their needs; (2) generative AI perpetuated biases and misrepresentations of intersecting identities; and (3) participants actively used everyday consumer technologies to foster agency, learning, caregiving, and cultural connection. Through sustained participatory engagement, the researchers identified methodological parameters to inform future disability-inclusive, participatory, and intersectionality-informed research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.094 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.015 | 0.048 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".