Relational Meanings of AI in Disability Care: An Intersectional, Arts-Based Inquiry
Bibliographic record
Abstract
Artificial intelligence (AI) is increasingly integrated into care systems, yet little is known about how care service providers perceive and respond to AI in their service provision in the context of supporting culturally and linguistically diverse migrants with disabilities. This study draws on an intersectionality-informed, arts-based research approach to explore how care providers make sense of AI, with attention to how their perceptions are shaped by social identities, professional experiences, and media narratives. A one-act play, constructed from data collected through participatory workshops with 15 care providers, illustrates that participants engage with AI as a relational, emotionally charged, and socially situated phenomenon. Their understanding reflected intersecting experiences of racialization, migration, gender, and labor precarity, as well as exposure to dominant media portrayals of AI. Their narratives showed a mix of fear, ambivalence, and cautious optimism rooted in concern about job security and loss of relational care, alongside hopes that AI might enhance accessibility and reduce human error. The play-based format captured the dialogic, affective, and embodied dimensions of participants' meaning-making, challenging technocratic and disembodied ways of knowing about AI and care. Findings suggest that inclusive and reflective spaces are critical for care providers to engage meaningfully with AI technologies and that intersectionality must inform the design, governance, and implementation of AI in care settings.
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 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 | 0.029 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 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 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".