The other self in free fall: Anxiety and automated tracking applications
Bibliographic record
Abstract
Recent scholarship on the rise of automated self-tracking has focused on how technologies such as the Fitbit and applications such as Nike+ demand that the user internalize the logic of contemporary surveillance. These studies emphasize the disciplinary structure of self-tracking - noting that these applications rely on logics of self-control, flexibility and quantification to produce particular neoliberal subjects. Following these readings, this paper considers the central role that anxiety plays in motivating, and maintaining, the subject's desire to understand the self through automated tracking systems. I will elaborate on this anxiety in three defined sections. Firstly, I will provide a brief overview of the relationship between anxiety and affect developed in both Freud's and Lacan's work on anxiety. Secondly, I will consider how the particular aesthetic principles of two applications, the Nike+ running application and the Spire breath monitoring application, afford the production of anxious digital selves by drawing on the emerging digital aesthetic of the free-fall in order to create a simultaneous distanciation and conflation of the embodied self and the digital self. Finally, I will consider how self-tracking applications represent a particular affective loop, fuelled by the subject's insatiable jouissance, which drives a never-ending anxious attempt to reunite the subject and object. Ultimately, it is from within these practices of digital self-construction that we can most clearly identify both an everyday anxiety of the self and emergent subjectivity and aesthetic of the present.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".