The other self in free fall: Anxiety and automated tracking applications
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
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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