Co-Design as Learning: The Differences of Learning When Involving Older People in Digitalization in Four Countries
Bibliographic record
Abstract
Involving older people through co-design has become increasingly attractive as an approach to develop technologies for them. However, less attention has been paid to the internal dynamics and localized socio-material arrangements that enact this method in practice. In this paper, we show how the outcomes that can be achieved with user involvement often pertain to learning, but their content can differ significantly based on how the approach is implemented in practice. Combining explorative, qualitative findings from co-design conducted in four countries (Canada, the Netherlands, Spain, and Sweden), we illustrate how different types of learning occurred as design workshops engaged the experiences and skills of older people in different ways. Our findings make visible how learning can be a core outcome of co-design activities with older adults, while raising awareness of the role of the power relations and socio-material arrangements that structure these design practices in particular ways. To benefit from the full wealth of insights that can be learned by involving older people, deeper knowledge is needed of the implicit features of design, the materials, meanings, and power aspects involved.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| 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".