Step, step, rest, step: challenging age-related norms and biometric bodies through self-tracking data-rematerialization
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.
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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 | 0.028 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".