Re-thinking human-computer interaction research and design with a growing ageing population: widening contexts of technology use, changing the subject and object of design
Bibliographic record
Abstract
This dissertation analyses, reflects on, and re-thinks the way in which Human-Computer-Interaction (HCI) research is conducted with a growing ageing population. This dissertation draws upon a 5-year research-through design study that combined ethnography and participatory design to explore the use and design of technologies aimed to enhance the social life of older people in civic contexts. The findings show a varied, proactive, dynamic and mutually shaping relationship between older people and digital technologies. This dissertation argues that this relationship challenges current ways in which older people and technologies are theorized within HCI. The results highlight the relevance of considering the communities in which older people interact in their daily lives in order to better understand their relationship with interactive technologies and design new digital artefacts that they find worthy of appropriation. By drawing upon the findings and theoretical discussions of dominant approaches in HCI research with older people, the dissertation proposes a re-formulation of fundamental aspects of thinking about and conducting HCI research and design with a growing and heterogeneous ageing population. Central to this re-formulation is to (a) widen the contexts of ICTs use by conducting more HCI research in civic contexts, (b) change the object of design, shifting the focus from defining the features of a technological artefact to fostering a mutual shaping relationship between technologies and everyday practices, and (c) re-think the subjects of design by moving from designing “for older people” to designing for “situated communities”. Keywords: HCI, older people, research-through design, ethnography, participatory design, technology appropriation, communities, civic contexts
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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 | 0.095 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.045 |
| Scholarly communication | 0.024 | 0.034 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".