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Record W4412843919 · doi:10.1177/10126902251358504

‘The watch is wrong today’: Older Canadians’ technologically mediated experiences of running

2025· article· en· W4412843919 on OpenAlexaff
Estée Fresco, Jesse Patricia Espiritu

Bibliographic record

VenueInternational Review for the Sociology of Sport · 2025
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsYork University
Fundersnot available
KeywordsSociologyPublic relationsMedia studiesAestheticsPolitical scienceGender studiesPsychologyArt

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.327
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

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