Productive self/vulnerable body: self-tracking, overworking culture, and conflicted data practices
<|reference_start|>Productive self/vulnerable body: self-tracking, overworking culture, and conflicted data practices: Self-tracking, the collection, analysis, and interpretation of personal data, signifies an individualized way of health governance as people are demanded to build a responsible self by internalizing norms. However, the technological promises often bear conflicts with various social factors such as a strenuous schedule, a lack of motivation, stress, and anxieties, which fail to deliver health outcomes. To re-problematize the phenomenon, this paper situates self-tracking in an overworking culture in China and draws on semi structured and in depth interviews with overworking individuals to reveal the patterns in users interactions and interpretations with self-tracking data. It builds on the current literature of self-tracking and engages with theories from Science and Technology Studies, especially sociomaterial assemblages (Lupton 2016) and technological mediation (Verbeek 2005), to study self-tracking in a contextualized way which connects the micro (data reading, visualization, and affective elements in design) with the macro (work and workplaces, socioeconomic and political background) contexts of self-tracking. Drawing on investigation of the social context that users of self-tracking technologies internalize, reflect, or resist, the paper argues that the productivity and value oriented assumptions and workplace culture shape the imaginary of intensive (and sometimes impossible) self-care and health, an involution of competence embedded in the technological design and users affective experiences. Users respond by enacting different design elements and social contexts to frame two distinctive data practices of self-tracking.<|reference_end|>
@article{zheng2024productive,
    title={Productive self/vulnerable body: self-tracking, overworking culture, and
  conflicted data practices},
    author={Elise Li Zheng},
    journal={arXiv preprint arXiv:2407.17618},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17618},
    primaryClass={cs.CY}
}