‘The watch is wrong today’: Older Canadians’ technologically mediated experiences of running
Bibliographic record
Abstract
This paper presents findings of interviews with Canadians runners aged 55 and older about their technology use. We explore how they use technology to navigate socio-cultural and bio-medical conceptualizations of aging as an unwanted process of decline. In doing so, we respond to a recognized gap in research on the intersection of aging, sport and the quantified body. Using a socio-materialist framework, we show that participants’ understandings of their physical capacities were co-produced with their digital devices, data, running shoes, active aging discourse, sensory experiences, gendered domestic and professional roles and the physical environments in which they run. Participants questioned the idea that the data they collected was an objective and accurate measure of their physical capacities. Additionally, some used sports gear to adapt to age-related changes in their bodies and others avoided using digital devices when they detracted from the pleasure of running. We argue that conceptualizing older runners’ technology use as part of an assemblage of agentic matter creates new possibilities for physically active older adults outside of limiting socio-cultural and biomedical ways of defining the right and wrong way to age. By making this argument, we contribute to research on the following topics: sport-related wearable technology; the sensory features of physical activity; and the intersection of pleasure, aging and physical activity.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".