TRACKED AND FIT: FITBITS, BRAIN GAMES, AND THE QUANTIFICATION OF THE AGING BODY
Bibliographic record
Abstract
This paper explores the technical turn to new ways of quantifying and standardizing measurements of age as these intersect with discourses of anti-aging and speculative futures of ‘smart’ quantified aging bodies. Often couched in a metaphorical language of ‘smart’, ‘fit’, ‘boosting’ and ‘optimizing’, the aging body is emerging as a node for data collection, monitoring, and surveillance. The research is located in the current literature that links aging, bodies and technologies, with specific extended examples of wearable devices such as fitness trackers and digital exercises and games designed for memory performance. Original research data are drawn from an analysis of design, marketing and promotional materials as well as qualitative user interviews. Based on such data, conclusions suggest that new technologies around aging and quantifiable fitness create an ambiguous image of the aging body and mind as both improvable but also inevitably in decline.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| 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".