Becoming Your Own Device: Self-Tracking Challenges In The Workplace
Bibliographic record
Abstract
Workplaces have long sought to improve employee productivity and performance by monitoring and tracking a variety of indicators. Increasingly, these efforts target the health and wellbeing of the employee – recognizing that a healthy and active worker is a productive one. Influenced by managerial trends in personalized and participatory medicine (Swan 2012), some workplaces have begun to pilot their own programs, utilizing fitness wearables and personal analytics to reduce sedentary lifestyles. These programs typically take the form of gamified self-tracking challenges combining cooperation, competition, and fundraising to incentivize participants to get moving. While seemingly providing new arrows in the bio-political quiver – that is, tools to keep employees disciplined yet active, healthy yet profitable (Lupton 2012) – there is also a certain degree of acceptance and participation. Although participants are shaped by self-tracking technologies, “they also, in turn, shape them by their own ideas and practices” (Ruckenstein 2014: 70). In this paper, we argue that instead of viewing self-tracking challenges solely through discourses of power or empowerment, the more pressing question concerns “how our relationship to our tracking activities takes shape within a constellation of habits, cultural norms, material conditions, ideological constraints” (Van Den Eede 2015: 157). We confront these tensions through an empiric case study of self-tracking challenges for staff and faculty at two Canadian universities. By cutting through the hype, this paper uncovers how self-trackers are becoming (and not just left to) their own devices.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.026 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".