Assembling the ‘Fitbit subject’: A Foucauldian-sociomaterialist examination of social class, gender and self-surveillance on Fitbit community message boards
Bibliographic record
Abstract
The rise of fitness-tracking devices such as the Fitbit in personal health and wellness is emblematic of the use of data-gathering health and fitness technologies by institutions to create a surveillance regime. Using postings on Fitbit community message boards and the theoretical frames of Michel Foucault and sociomaterialist scholars, the goal of this article is to analyse the experiences of those who choose to self-track using a Fitbit and the constellation of barriers and facilitators (human and non-human) related to social class and gender that enable and constrain one's ability to use a Fitbit as intended. First, we examine the social class assumptions of Fitbit as a risk management tool in the workplace, illustrating what elements must come together - both human and non-human - to create an environment that enables walking throughout the workday to combat the risks of sedentary work. Second, we explore the ways that Fitbit users 'confessed' to their past inactivity and how gendered home labour differently enables and constrains some of the users' abilities to act on their confessions. Ultimately, one's ability to engage in the idealized use of the Fitbit in the minds of its users, or what we term the 'Fitbit subject assemblage', is structured by numerous material and social factors that must be taken into account when examining the mechanics of power in fitness tracking.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.037 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".