Becoming Your Own Device: Self-Tracking Challenges In The Workplace
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it