You want a hot body? You want a Bugatti? You better work(out): FitBit, neoliberalism, and the thin ideal
Bibliographic record
Abstract
The Fitbit manifests an ideology of healthism that prioritizes the pursuit of physical health above all else. The device’s design, use, and underlying epistemic frameworks transform exercise into data, labour, and knowledge, respectively. Using an accelerometer and green LED lights, the Fitbit translates the movements of human bodies into data. ‘Exercise’ is thus limited to what can be mechanically registered and algorithmically sorted into a pre-set category. This freely generated user data is aggregated into profitable datasets that Fitbit can sell to advertisers. Fitbit’s partnerships with insurers or employers further exploit workers by penalizing non-participants and users who generate undesirable data. Finally, the practice of activity tracking frames exercise as a health intervention and restricts the possibility of being absent from one’s body. Furthermore, Fitbit understands fitness through the lens of weight management, where the fit body is a conspicuously self-disciplined (read: thin) body. By framing fitness as a choice, individuals are held personally responsible for health outcomes, and being ‘unfit’ reflects a physical and moral failure. The insights produced by Fitbit thus restrain and shape users’ self-knowledge, perpetuating a cultural norm that understands ‘fit’ bodies as healthy, productive, and morally good.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.063 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".