Connections, negotiations, and tensions: Talking tech with older adults
Bibliographic record
Abstract
Mobile media, and the larger digital technological systems of which they are a part, both shape and are shaped by contemporary experiences of aging. With the aim of exploring older adults' understandings, uses of, and experiences with digital technologies in their everyday lives, we conducted four exploratory focus groups in two Canadian cities, with a total of 29 participants representing a diverse range of ages, living situations, and socio-economic statuses. Challenging stereotypes of technophobic or health-obsessed elders, our participants reported using a wide variety of devices and apps for a multiplicity of purposes. Focus groups were characterized by open-ended discussion, eliciting complexity, creativity, and agency in our participants' understandings and experiences of their digital worlds. Two main themes emerged from the analysis. First, a number of tensions - self-talk vs practice, design vs adaptation, "scripts" vs recreation - were articulated. Second, participants recounted the complex negotiations between technologies and people, bodies, environments, and resources that conjoined to shape their navigations of digital worlds. We suggest that open-ended dialogue with older adults is a promising method for understanding their ongoing, complex, and socially and materially situated engagements with technology. As a generative methodological tool, the focus group not only captures these dynamics but also produces the frictions, negotiations, and shared reflections that reveal them.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.016 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 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".