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Abstract
This paper explores the specific questions raised for social epistemology encountered in code and software. It does so because these technologies increasingly make up an important part of our urban environment, and stretch across all aspects of our lives. The paper introduces and explores the way in which code and software become the conditions of possibility for human knowledge, crucially becoming computational epistemes, which we share with non-human but crucially knowledge-producing actors. As such, we need to take account of this new computational world and think about how we live today in a highly mediated code-based world. Nonetheless, here I want to understand software epistemes as a broad concept related to the knowledge generated by both human and non-human actors. The aim is to explore changes that are made possible by the installation of code/software via computational devices, streams, clouds or networks. This is what Mitcham calls a “new ecology of artifice”. By exploring two case studies, the paper attempts to materialise the practice of software epistemologies through a detailed analysis. This analysis is then drawn together with a notion of compactants to explore how studying tracking software and streams is a useful means of uncovering the agency of software and code for producing these new knowledges.
Notes
[1] An n-gram is a list of “n” items from a given sequence of textual materials or speech. The basic units can be letters, words, syllables, etc. Google n-gram viewer is a good example of using this technique to search textual corpora: http://books.google.com/ngrams.
[2] Naturally this includes all forms of knowledge encoded as textual, video, film, photography and so forth. New forms of “mash-up” data platforms are constantly emerging, such as Google Maps and Mixel, which enable very sophisticated knowledge and metadata to be combined in interesting ways.
[3] These include HTTP cookies and Locally Stored Objects (LSOs) and document object model storage (DOM Storage).
[4] Here, I am concerned with the collection of data through web bugs and bracket out other kinds of “malware” such as botnet, Trojan, viruses and so forth. An interesting Q&A with a botnet hacker, which demonstrates the extent of vulnerability of the average computer user is described in throwawayCitation236236 (2012).
[5] Cookies are small pieces of text that servers can set and read from a client computer in order to register its “state”. They have strictly specified structures and can contain no more than 4 KB of data each. When a user navigates to a particular domain, the domain may call a script to set a cookie on the user’s machine. The browser will send this cookie in all subsequent communication between the client and the server until the cookie expires or is reset by the server (Mittal Citation2010, 10).
[6] Ghostery describes itself on its help page: “Be a web detective. Ghostery is your window into the invisible web – tags, web bugs, pixels and beacons that are included on web pages in order to get an idea of your online behavior. Ghostery tracks the trackers and gives you a roll-call of the ad networks, behavioral data providers, web publishers, and other companies interested in your activity” (Ghostery Citation2012a). See also https://disconnect.me/.
[7] For an example see, http://static.chartbeat.com/js/chartbeat.js.
[8] Also see examples at: (1) Chartbeat: http://static.chartbeat.com/js/chartbeat.js; (2) Google Analytics: http://www.google-analytics.com/ga.js; (3) Omniture: http://o.aolcdn.com/omniunih.js and (4) Advertising.com: http://o.aolcdn.com/ads/adsWrapper.js.
[9] For example the page-scraping of data from open access web pages using “robots” or “spiders” in order to create user repositories of data through aggregation and data analysis. Interestingly this is the way in which Google collects the majority of the index data it uses for its search results. This is also becoming a digital method in the social sciences and raises particular digital research ethics that have still to be resolved, see https://www.issuecrawler.net/, http://socscibot.wlv.ac.uk/, http://webatlas.fr/wp/navicrawler/.
[10] See these commercial examples of user control software for controlling user public exposure to trackers, web bugs and compactants, although the question is raised as to why you would choose to trust them: https://cloudcapture.org/register/ and http://www.abine.com.
[11] See http://quantifiedself.com/.
[12] An example of pre-computational collection of data about the self as a lifestream is represented by Roberts (Citation2004). One of the criticisms that recur in the peer-review section is that Roberts fails to account for his own anticipation of his experimentation and previous experimentation colouring his results. Nonetheless, this kind of self-knowledge through collection is made both easier, and arguably more rigorous by the collection through compactants. Especially, if the collection is of wide rather than narrow width, it enables a post hoc analysis and hypothesis surfacing to occur. Clearly, compactants also make the collection far easier with mobile devices.
[13] Wolfram further writes: “It’s amazing how much it’s possible to figure out by analyzing the various kinds of data I’ve kept. And in fact, there are many additional kinds of data I haven’t even touched on in this post. I’ve also got years of curated medical test data (as well as my not-yet-very-useful complete genome), GPS location tracks, room-by-room motion sensor data, endless corporate records—and much much more … And as I think about it all, I suppose my greatest regret is that I did not start collecting more data earlier. I have some backups of my computer filesystems going back to 1980. And if I look at the 1.7 million files in my current filesystem, there’s a kind of archeology one can do, looking at files that haven’t been modified for a long time (the earliest is dated June 29, 1980)” (Wolfram Citation2012).
[14] Some examples of visualisation software for this kind of lifestreaming quantification and visualisation are shown on these pages from the Quantified Self-website: Personal Data Visualisation, Jaw-Dropping Infographics for Beginners, A Tour Through the Visualisation Zoo, Visual Inspiration.
[15] See http://open.sen.se/ for a particularly good example of this: “Make your data history meaningful. Privately store your flows of information and use rich visualizations and mashup tools to understand what’s going on” (Sense Citation2012).
[16] Computational actants, drawing the notion of actant from actor–network theory.
[17] Of course compactants are not just “internal” data collection agents. They may also be outside of your data resources and networks probing to get in, this kind of unauthorised access to personal data is on the rise and has been termed the industrialisation of data theft (see Fulton Citation2012). Indeed, Fulton argues that “scripts, bots, and other non-social means for obtaining access [to data] remains statistically more effective than direct, personal contact - although even these automated means remain astoundingly simple” (Fulton 2012).
[18] For example see Bamford (Citation2012) who writes about the Utah Data Center is being built for the National Security Agency: “A project of immense secrecy, it is the final piece in a complex puzzle assembled over the past decade. Its purpose: to intercept, decipher, analyze, and store vast swaths of the world’s communications as they zap down from satellites and zip through the underground and undersea cables of international, foreign, and domestic networks. The heavily fortified $2 billion center should be up and running in September 2013. Flowing through its servers and routers and stored in near-bottomless databases will be all forms of communication, including the complete contents of private emails, cell phone calls, and Google searches, as well as all sorts of personal data trails—parking receipts, travel itineraries, bookstore purchases, and other digital ‘pocket litter’. It is, in some measure, the realization of the ‘total information awareness’ program created during the first term of the Bush administration—an effort that was killed by Congress in 2003 after it caused an outcry over its potential for invading Americans’ privacy” (Bamford Citation2012).
[19] What we might call “outsider code” or “critical code” is an interesting development in relation to this. A number of websites offer code that data-scrapes, or screen-scrapes information to re-present and analyse it for the user, some examples include: (1) Parltrack software, which is designed to improve the transparency of the EU parliamentary legislative process, http://parltrack.euwiki.org/ and (2) TheyWorkForYou, which screen-scrapes the UK Parliamentary minutes, Hansard, http://www.theyworkforyou.com/.
[20] Here I tentatively raise the suggestion that a future critical theory of code and software is committed to unbuilding, disassembling, and deformation of existing code/software systems, together with a necessary intervention in terms of a positive moment in the formation and composition of future and alternative systems.