This wiki repertories and tries to translate the evolutions of the quantified self processes. It is correlative of women user groups that are organised to research and play with those elements of data that we capture from our tracking devices.
I would be great to hear your voice on this subject, please feel contribute to make an account natacha at lesoiseaux io you will then be able to edit this wiki.
Health trackers help you track and measure specific events of your daily life, mainly the number of steps that you make, but also your heart beat, your activity level eventually deducing your sleeping time, and finally updating your calories or water intake.
All this information is then algorithmically organised demonstrated and quantified. One can ask what is the scope of this quantification process that discriminates certain specifics of the body in other words, it seems clear that quantified self technologies are simply not designed with a full range of bodies in mind.
Body information becomes data, at the same time, although social contexts are abstracted away, inequalities are solidified and aggravated by means of their lack of representational presence. If inequalities, gender based or otherwise, are not represented in data, then it is difficult to address them, but that does not mean that they are not present.
The primary argument is that modern surveillance systems operate upon logics of disembodied control at a distance. As such, they artificially abstract bodies, identities, and interactions from social contexts in ways that both obscure and aggravate gender and other social inequalities
Further more quantifie self procedures drive on a terrain of feminist self help that is not profiting from the Web 2.0 processes that mostly interfers with it by apropriating its langages and processesto the benefit of corporations. http://www.lesoiseaux.io/doku.php?id=feminism_and_the_quantified_self
SLOGAN : And I like winning. I want my life to be more like Mario’s in Super Mario Bros 3.
1° Possible Rules for quantified gaming
– blue dots for fitness steps
– red dots for good actions
– purple for job related responsibility
2° Exchange/Black Market fitness data
– I need 5 green dots who how can I pay for them
3° Cheating tricks
– I promised my wife I would loose weight, and did not do anything how to fake my data to show it is not my responsibility.
4° Have success weeks and milestone prizes. Like any game, you need zesty goals. These are long-term benchmarks that keep me engaged in gamification.
Latania Sweeney about aggregating health data
It’s become clear that these protections fall short of what is needed to provide protection, especially because health data is often generated outside of the contexts where these acts apply, and because sensitive medical conditions can be inferred from non-medical data, like consumer habits.
According to research by Latanya Sweeney, anonymized medical records can be re-identified by triangulating patient information with other data, newspaper events describing patient names and incidents that resulted in a particular medical injury.
She also discovered that “87% (216 million of 248 million) of the population in the United States had reported characteristics that likely made them unique based only on 5 digit ZIP, gender, date of birth.” which means that their anonymized medical records can be matched with other databases to re-identify them.
In such a context it is worth thinking twice before aggregating public heath data, because under the apparent utility and research necessity, hidden agendas can easily nest .
Therefore Latania Sweeney directly asks: Given the diversity of data that can be useful in a health and medical context, is the construct of “health data” helpful? If so, for what purposes and how should this concept be constructed?
What are the appropriate avenues for addressing potential risks to individuals in non-medical contexts stemming from the availability of their health data?
Antoinette Rouvroy calls for “recalcitrance”
I have been trying to encompass the scope of the current trend in self quantification that translates through an important rise of new mobile sensing gears, heavy corporate communication, and DIY self organised movement, as it slips quickly towards the idea of a possible e-health raising many questions around identity privacy and the public sphere.
I feel that this self quantification movement needs to be firstly addressed as an online social trend from a critical feminist approach. It is indeed a new representational form of bodies, that tends to the idea of an individualized and normalized persona reached through the use of intrusive technologies, how do they relate to our previous digibodies (Flanagan 2003)?
I understand this use of sensors as a form of body mapping strongly binded to a social structure inforced by corporate and technological powers that quickly accelerate its implementation as a playfull and
I will make mine the aparatus of Antoinette Rouvroy when she calls for “recalcitrance” Antoinette Rouvroy: ” les systèmes de contrôle de la gouvernance libérale” “banalité sécuritaire” “qu’à condition de disposer de quantités massives de données (à caractère personnel ou non) relatives aux individus et de pouvoir appliquer sur ces quantités massives de données des algorithmes de calcul statistique qui permettent d’établir « automatiquement », des corrélations significatives entre ces données recueillies dans des contextes hétérogènes les uns aux autres, il devient possible de TOUT prédire” “En appeler à la récalcitrance, c’est, ici, rappeler l’irréductibilité des personnes aux réseaux de données digitalisées à travers lesquels le pouvoir (quel qu’il soit, public, ou privé) ”
I thought you might like the irony of the timeline, from experiential situationiste thrrough to actual real life social experimentation by data collection. Same issue happens with body data that has started as a playful endeavor a gaming environment at the city scale, and that now finds new effectiveness through web 2.0 social network.