The fitness boom and the pursuit of body ideals: domesticating the use of smartwatches in Ghanaian fitness spaces
Bibliographic record
Abstract
This study explores the use of smartwatches within fitness spaces in Ghana. As part of the fitness boom, the pursuit of body ideals has become increasingly intertwined with digital self-tracking, health, and moral imaginaries of self-improvement. The use of smartwatches in fitness spaces is crucial for understanding these connections. The study draws on interviews and participant observations with fitness enthusiasts in gyms and keep-fit clubs. We employ domestication theory to examine how smartwatches influence users’ understanding and construction of their fitness identities, social identities, and body ideals. Using these ethnographic methodologies and reflexive thematic analysis, our study reveals that smartwatches shape the Ghanaian understanding and construction of fitness and social identities. Practically, the participants have domesticated smartwatches to function as an infrastructure that promotes an active lifestyle. Symbolically, the device represents their socio-economic class and commitment to self-improvement. The findings suggest that as Ghanaian fitness enthusiasts strive to meet their fitness goals and conform to societal expectations of the ideal body, the smartwatch becomes a proxy for fitness, body ideals and communication convenience.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".