Digital Traces in Context| Digital Traces and Personal Analytics: iTime, Self-Tracking, and the Temporalities of Practice
Bibliographic record
Abstract
This article examines digital traces related to the use of self-tracking devices in the context of digitally mediated iTime. These devices enable the continual production, representation, interpretation, and negotiation of varied traces of physical activity, time use, and temporal coordination. We focus on temporalities, exploring how the “tendencies” of iTime are being differentially produced, encountered, interpreted, and acted on in daily life. In-depth interviews with 25 individuals between 18 and 24 years of age are used to examine the contexts of trace production and analysis as they take place within different configurations of ordinary practice. First, we examine whether continuously self-tracked data alters people’s sense of the temporal possibilities of self-transformation. Second, we ask whether people’s encounters with, and analytics of, their traces alter how their daily life is temporally sequenced, coordinated, and experienced. Third, we consider if and in what ways quantified and visualized self-tracked data change the temporal meaning and value of media-related practices for those undertaking them. We show how digital traces are produced within, and become concrete elements of, the temporalities of practices.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".