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.
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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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.019 | 0.068 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.005 |
| 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".