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Technologies for Sharing: lessons from Quantified Self about the political economy of platforms
Quantified Self (QS) is a group that coordinates a global set of in-person meetings for sharing personal experiences and experiments with self-tracking behaviours, moods, and activities. Through participation in US-based QS events and watching online QS presentations from around the globe, we identify a function of ambiguous valuation for supporting sharing communities. Drawing on Stark’s (2011) theory of heterarchy, we argue that the social and technical platforms supporting sharing within the QS community allow for multiple, sometimes conflicting, sets of community and commercial values. Community cohesion benefits from ambiguity over which values set is most important to QS members. Ambiguity is promoted by sharing practices through at least two means, the narrative structure of members’ presentations, and what counts as tracking. By encouraging members to adhere to a three-question outline, the community ensures that multiple values are always present. Thus, it becomes a question of which values this sharing community emphasizes, not which value sets members present, at any given time. By leaving the tools and methods of tracking open − from sophisticated wearables and data analysis to pen-and-paper and storytelling − the community creates space for and embraces self-trackers with a broad spectrum of technological proficiency and interest. QS as a group capitalizes on circulation of knowledge valued somewhat ambiguously to sustain and grow the community, both encouraging and supporting the commercialization of self-tracking technologies while keeping technology developer interests from overwhelming community-building interests. This, we argue, has implications for researchers hoping to understand online communities and the ‘sharing economy’ more generally.
Kristen Barta & Gina Neff. (2015). Technologies for Sharing: lessons from Quantified Self about the political economy of platforms. Information, Communication & Society. DOI:10.1080/1369118X.2015.1118520
Data Empathy: Learning from health care
I just published an essay on Medium about what the dinosaurs of health care can teach health startups.
Nude pictures of celebrities stolen from their own iCloud accounts. Facebook experimenting with the emotions in their users’ feeds. Google reading Gmail before their users do. Fitness trackers without privacy policies, vulnerable to security breaches, and bait-and-switch tactics to sell customers’ data. Almost every day there is a story about the gap between the expectations people have for their own data and what companies actually do with that data. To fix this gap, we first need to rethink the nature of data.
Continue reading at Medium:
Why Big Data Won’t Cure Us
The biggest challenge for the use of “big data” in health care is social, not technical. Data-intensive approaches to medicine based on predictive modeling hold enormous potential for solving some of the biggest and most intractable problems of health care. The challenge now is figuring out how people, both patients and providers, will actually use data in practice.
Big Data, September 2013, 1(3): 117-123. doi:10.1089/big.2013.0029.