Many Forms of "Good Ageing"
Bibliographic record
Abstract
Digital health and social care innovations for older people embody specific ideas about “good ageing.” But what does good ageing mean to older adults themselves? And how do their ideas and practices relate to the ideas of good ageing that have informed the design of those digital tools that they are invited to engage with? Our research comic explores these questions by drawing on eight months of ethnographic fieldwork in three innovation initiatives that trial and implement health and social care innovations for older people in Italy, Spain, and the United Kingdom. Presenting our research in the format of a research comic is an explicit attempt at sharing everyday experiences of ageing and technology with a broader audience including researchers but also older adults, city and community councils, social workers, and technology developers with whom we collaborated in the field. The drawings, fieldwork quotes, and accompanying reflections illustrate the diverse and sometimes conflicting forms of good ageing that shape users’ engagement with proposed technologies. As such, the research comic invites the reader to question dominant perceptions of technologies as simple tools that facilitate good ageing. It highlights the importance and value of geographical, cultural, and affective closeness to the everyday lives of those for and with whom these technologies are designed. Such closeness, we argue, is a first step in being able to notice conflicts between different forms of good ageing and to adjust digital tools and services in such a way that they facilitate forms of good ageing that older adults themselves find relevant.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| 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".