Reblog > Why measure ourselves?

Anne Galloway wrote an excellent blogpost (in 2011!) about the ‘quantified self’ movement that I feel sums up a lot of how I feel about a creeping orthodoxy of practices of (uncritical) measurement. Great stuff, so reblogged here:

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“I demand, I insist, that everything around me shall henceforth be measured, tested, certified, mathematical, and rational. One of my tasks must be to make a full survey of the island, its distances and its contours, and incorporate all these details in an accurate surveyor’s map. I should like every plant to be labeled, every bird to be ringed, every animal to be branded. I shall not be content until this opaque and impenetrable place, filled with secret ferments and malignant stirrings, has been transformed into a calculated design, visible and intelligible to its very depths!”

–Michel Tournier, Friday; or, The Other Island (Vendredi ou les Limbes du Pacifique), 1967

The quote above captures an attempt to stave off madness, and it’s the first thing that comes to mind whenever someone brings up data-driven science orthe quantified self movement. Now I don’t mean to suggest that either of these activities is mad, but the quest to measure everything makes me nervous, even while it fascinates me.

Robert P. Crease, author of World in the Balance: The Historic Quest for an Absolute System of Measurement, wrote an interesting piece for the NYTimes this weekend on measurement and its discontents. In it he describes two different ways of measuring things:

“In one kind of measuring, we find how big or small a thing is using a scale, beginning point and unit. Something is x feet long, weighs ypounds or takes z seconds. We can call this ‘ontic’ measuring, after the word philosophers apply to existing objects or properties. But there’s another way of measuring that does not involve placing something alongside a stick or on a scale. This is the kind of measurement that Plato described as ‘fitting.’ This involves less an act than an experience: we sense that things don’t ‘measure up’ to what they could be. This is the kind of measuring that good examples invite. Aristotle, for instance, called the truly moral person a ‘measure,’ because our encounters with such a person show us our shortcomings. We might call this ‘ontological’ measuring, after the word philosophers use to describe how something exists.

It’s obvious which kind of measurement is preferred or privileged in the examples I provided above, and Crease explains:

“As the modern world has perfected its ontic measures, our ability to measure ourselves ontologically seems to have diminished. We look away from what we are measuring, and why we are measuring, and fixate on the measuring itself “¦ [But] in our increasingly quantified world, we have to determine precisely where and how our measurements fail to deliver. Now that we have succeeded in defining the kilogram by an absolute universal standard, we still have to remind ourselves of the human purposes that led us to create the kilogram in the first place, and always to make sure that the kilogram is serving us, and not the other way around.”

So why do we measure ourselves? The Quantified Self website tagline is “self knowledge through numbers” but the purpose or benefit of this knowledge is less clear. Ethan Zuckerman, who attended the recent Quantified Self Conference, notes that people self-track in order to “test the effectiveness of an intervention” or “monitor and understand the dynamics of a particular indicator,” and that one of the benefits might be that a “personal science could help a much broader range of people.” However, he also points out that “most self-trackers aren’t sharing their data very widely, both due to privacy concerns (will my health insurance provider cut me off if they discover I’m a restless sleeper? That I only walk 3000 steps a day?) and in part because sharing and aggregating data may not have easily apparent benefits.” This seems to suggest that if the data collected are mostly useful to the person collecting them, then the primary benefit is self-improvement or self-management. Zuckerman distinguishes this from surveillance data that are useful to others, but in my mind, self-surveillance is still surveillance. And, honestly, I’m really not a fan of self-improvement if it seeks to make people as efficient and productive as industrial machinery, or if it is considered to be a direct path to moral righteousness.

The quantified self, especially as a form of citizen science, is most often presented in terms of increased or improved agency. But I wonder what this agency actually allows people to do or be? How does it differ from what Foucault described as disciplinary power, and a relation of docility and utility with our bodies? (In the scenarios he described, internalised discipline removes the need for externalised force and violence because monitoring and regulation are self-imposed.) Put more bluntly, if our bodies are still just cogs in a machine then being the machine operator is surely only a limited improvement, or lateral shift in power, rather than a revolutionary change in the order of things.

For that kind of wholesale change to occur, I think we need to recapture our “ontological” measuring capacities and capabilities. Of course it’s ludicrous to claim that ontic measurements and quantifiable data are useless, but as I’ve argued before, “by implicitly supporting the notion that scientific data are the [most] appropriate types of evidence a citizen can collect, political action relies on conformity to existing structures of knowledge and power.” And the potential for action is also further complicated by “the capacity (or incapacity) of people to make sense of the data collected, not to mention their willingness (or unwillingness) to act as data collectors in the first place.” What I want to suggest is that we also need other ways of measuring that are equally valued. Ways that are more experiential, more qualitative and more ambiguous–and therefore more inviting of critical interpretation and debate.

What do others think?

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Anne got some responses which are worth checking out, here.

Six myths about ‘big data’, Kate Crawford

[via Rob Kitchin’s Programmable City feed]

In a recent workshop at the UC Berkeley iSchool, Kate Crawford of Microsoft Research (and several other institutions) offered a keynote critically reflecting upon the discursive regimes that have been built in the understandings of ‘big data’. Riffing on Claude Levi-Strauss’ ‘mythologies’ project, Crawford’s talk, ‘The Raw & the Cooked’, outlines six myths about ‘big data’ and unpicks the assumptions upon which these myths rely. In brief, Crawford’s six myths are: ‘Big data are new’, ‘Big data is objective’, ‘Big data don’t discriminate’, ‘Big data makes cities smart’, ‘Big data is anonymous’, ‘You can opt out of big data’.

Crawford does a good job at setting the scene for a range of work that touches on the uses of large data sets that come from a range of sources, that may be repurposed for rather different ends than those intended, about which academics, politicians, civil servants and business people have told, and reinforced, particular kinds of stories such that those data have become very significant. What Crawford helpfully does is step back from particular data-oriented agendas, such as ‘smart cities’ or ‘social network analysis’, to highlight some particular, perhaps pressing, methodological and ethical concerns. This is of particular interest for me in relation to a project I am beginning to undertake with Steve Hinchliffe concerning ideas around ‘contagion’, looking in particular at social media data.

Crawford gives a good namecheck in the section on the fourth myth for Matt Zook too!

Close up at a distance, Laura Kurgan

There are a number of people engaging in really thoughtful and thought-provoking artistic, activist and political engagements/interventions with geographical (topographical) technologies, which are very much worth paying attention to. Work by Jordan Crandall, Laura Kurgan, Lisa Price and Trevor Paglen, amongst others, I think offers some really interesting ways of critically thinking about, and engaging with, the various kinds of mappings we (as socieities) are variously undertaking for sometimes not-so-benevolent causes.

Laura Kurgan’s recent Close Up at a Distance: Mapping, Technology and Politics has attracted some deserved attention by geographers and, in a similar vein to Patrick Crogan’s excellent Gameplay Mode, offers an interesting critical engagement with the military origins of satellite mapping and imaging technologies. Kurgan argues that we cannot stand at a ‘critical distance’ to evaluate these technologies but rather –  we are “addressed by and embedded within them”. Thus – “Only through a certain intimacy with these technologies–an encounter with their opacities, their assumptions, their intended aims–can we begin to assess their full ethical and political stakes” (page 14).

Columba Peoples, from the School of Sociology, Politics and International Studies at the University of Bristol, has written a good review of Kurgan’s Close Up at a Distance for the open access book reviews section of Environment and Planning D: Society and Space, which is online here and well worth a read.

Also, Laura Kurgan and Trevor Paglen engaged in a fascinating conversation about their practice at Columbia University earlier this year, which was recorded and is available on YouTube:

Note: Trevor’s mic doesn’t work very well and he’s muffled for a bit at the beginning but it gets fixed so stick with it!

Bots book up top restaurant tables

The BBC carried a story in their technology section today about coders writing custom bot scripts that search for restaurant reservations and book tables before those looking for themselves can respond.

The story comes from a blog post by a chap called Diogo Monica, in Silicon Valley (where else), who noticed that tables at a good restaurant he likes were getting booked up by bots and so he decided to write one for himself. The BBC story narrates how:

The code emailed Mr Monica when other diners cancelled reservations or SBP released more tables. While the code helped him get a table now and then it quickly became ineffective. Close scrutiny of the [restaurant’s] website revealed why.

“I found myself looking at it and noticed that as soon as reservations became available on the website (at 04:00), all the good times were immediately taken and were gone by 04:01,” he wrote.

As with many other time sensitive transactions, in this fairly rudimentary software domain of online reservations a veritable arms race has ensued in the creation of more sophisticated and more rapid software programmes. The fairly arbitrary rules of booking, with automated update timings etc., become the terrain for battle. Where once the ‘who you know’ at the restaurant or in the right social circles would secure you a good table at the most desirable location and time it seems to have transformed, in some cases, into the ‘what you know’ of online booking systems code. As Mr Monica suggests:

“As for tactics, think of this war like high-frequency trading,” he said. “The people with the best algorithms/optimisations will have an edge over everyone else.”

Bonkers.

A comprehensive list of data sources and services

Reblogged from Thinking Culture:

Via Rob Kitchin on Twitter, the Visualising Data blog has an entry that is described as ‘A big collection of sites and services for accessing data‘. The post contains a comprehensive list of the data sources, and analysis tools, that are available. These are also helpfully categorised under headings. This is a really important development that could prove to be a key resource for researchers and students who are looking to use digital or ‘big’ data in their research. Here is the description of the list from the post:

‘This collection presents the key sites that provide data, whether through curated collections, offering access under the Open Data movement or through Software/Data-as-a-Service platforms.‘

Read more…

As David suggests, this looks very handy.