A transactional perspective on the everyday use of technology by people with learning disabilities
Bibliographic record
Abstract
Research points to the potential benefit of information and communication technologies (ICTs) for people with learning disabilities. However, there has been limited exploration of the interconnected nature of people and social context when considering how people with learning disabilities use ICTs. The result has been an overemphasis on (and assumptions of) the skill limitations and individual capacity of people with learning disabilities and their use of ICTs. Using a transactional perspective based on the work of John Dewey, this study aimed to explore the interrelationship of people with learning disabilities, ICTs, and the social world. Using a post-qualitative and theory-driven approach, we employed a transactional perspective (namely the interrelated concepts of embodiment, habit, and growth) to analyse interview data from 10 adult participants with learning disabilities. Our analysis suggested that when people have access and opportunity to co-mingle with technology, the technology can become embodied (feel like a part of them). ICTs were found to become an extension of participants’ bodies, enhancing their literacy, learning, and connection with others. Introducing the concept of conjoint action, we explored how human and nonhuman bodies are enmeshed in the formation of what we typically think of as human habits. Participants developed more-than-human habits of ICT use that they drew from to coordinate with their everyday life, navigating everyday challenges. Yet, in restrictive social contexts (or those influenced by underlying assumptions of the vulnerability of people with learning disabilities), participants were less likely to embody ICTs in the rhythms of everyday life and experience benefits of ICT use. Our findings shed light on the entangled, transactional relationship of people and the social world, and present occupation as the conjoint action of human and nonhuman bodies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".