Step, step, rest, step: challenging age-related norms and biometric bodies through self-tracking data-rematerialization
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
This paper presents a research-creation project that aimed to explore how the experience of self-tracking and the data retrieved from self-tracking activities could be used to creatively critique the norms and regulatory mechanisms embedded within self-tracking devices and practices as they come to intersect with pressures and injunctions lying on seniors’ bodies. More specifically, we discuss the processes of research and creation involved in the ‘Dancing with Fitbit’ project (http://labs.fluxo.art.br/dancing-with-fitbit/), oriented by the key question: How can we use data and the lived self-tracking experience to disrupt the biometric bodies produced by self-tracking technologies, as they intersect with ideals of ‘successful aging’? The article presents the processes at stake in the development of the project, so as to highlight how creation and research co-informed each other and to render explicit the tacit knowledge produced and embedded in the interrelated creative processes (Paquin, L.-C. 2018. Faire le récit de sa pratique de recherche-création. https://www.academia.edu/38426295/Faire_le_r%C3%A9cit_de_sa_pratique_de_recherche-cr%C3%A9ation). We first present the ‘creation-as-research’ (Chapman, O., and K. Sawchuk. 2012. “Research-Creation: Intervention, Analysis and ‘Family Resemblances’.” Canadian Journal of Communication 37 (1): 5–26. doi:10.22230/cjc.2012v37n1a2489) approach mobilized. We then present the subversive forms of data materialization (through choreography, sounds, visuals) we carried out and how they relate to the initial creative and critical intents formulated by the project. We conclude by highlighting the importance of the collaborative and processual character of the project.
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 | 0.004 |
| 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