DIGITAL TRACES, SOCIAL RESIDUES: USAGE AS SELF-REPRESENTATION ON LIFESTYLE APPS
Bibliographic record
Abstract
Self-representation is ubiquitous in digital spaces, but much of the existing literature discussing it is focused on social media contexts. Self-representation involves the creation of media traces that record users’ presence and existence through media, but these traces and the platforms that circulate them do not have to be explicitly or directly social. We study self-representation in alternative digital contexts: lifestyle apps focused on ambient usage behaviors that are generally private, personal, or invisible in online spaces. These apps incorporate social features based on displays of user data—streams repurposing users’ engagement with the platform as social displays to networked friends. The three apps of interest include the payment app Venmo, music streaming app Spotify, and fitness tracking app Strava. We study these apps through a combination of “walkthroughs” of each of the platforms and interviews with users. Our findings suggest that these platforms repurpose individual user behaviors as digital residues in social streams, transforming them into networked self-representations. Within this process, we expect that some users intentionally mobilize their engagement with the apps as social performances. Ultimately, this study raises questions about digital self-making, commodification, and platform sanctioned sociality beyond social media.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".