Infrastructure studies, platform studies, software studies, media studies, (critical) data studies – what are they about?!

I’ve been looking in several traditions and fields, some that are recurring are infrastructure studies, software studies, media studies, data studies (as well as the prefix ‘critical’ so several of them). I wanted to have a brief, and probably superficial look at what they are about and how I can demarcate them from one another. I assume that all of them are interdisciplinary, possibly complicating this endeavour 😀 But let’s give it a try:

Infrastructure Studies:

Infrastructure studies, emerging from Science and Technology studies and information sciences, focuses on analyzing large and essential sociotechnical systems. The analysis can entail telecommunication networks, power grids and possibly sewer systems, but also digital infrastructure. Overall infrastructure studies explore widely accessible and shared systems and services that are often provided by governments in the public interest. According to (Plantin et al., 2018) infrastructure studies developed along 2 lines; the first focuses on a historical perspective of large systems while the second highlights the sociology of infrastructure, also highlighting human elements such as work practices, habits, organizational culture etc. Hence, Infrastructure Studies simultaneously addresses the technical, social, and organizational aspects of the development, usage, and maintenance of infrastructures in local communities as well as global arenas. Further, this second line of thought often associated with Star and Bowker has highlighted key features of infrastructure such as ubiquity, reliability, invisibility, gateways, breakdown as well as infrastructure as learned in communities of practice.

Infrastructure studies explore the usually hidden, the things that are taken for granted and that operate in the background- or the “boring things” we all rely on and depend on, but few think or know about. Especially with the second line of infrastructure studies, the relational aspect of infrastructure becomes prevalent; there is no universal definition of infrastructure, as one person’s infrastructure might be another person’s topic or even difficulty.

Software Studies:

Software studies is an interdisciplinary field that focuses on the program/software, exploring software (systems) and their social and cultural effects. Overall, Software Studies look into how software is integrated into processes of contemporary culture and society. Software can be seen as an artefact and as a cultural practice, and in this context can include databases, interfaces, design, programming languages, platform infrastructure etc. While computer science might look at similar programs/software, computer science is primarily concerned with optimization and performance, while Software Studies highlights the cultural, political and social aspects of software.

Software studies are closely connected to the emerging field of digital humanities and recently, as I see it, also with Platform Studies.

Data Studies:

Data Studies focuses on the “social life” of data, particularly Critical Data Studies. The emerging interdisciplinary Critical Data Studies (coined by Dalton & Thatcher, 2015), therefore, aim at the systematic study of data and its criticism, primarily exploring ethical, cultural, and critical challenges posed by (Big) Data. Critical Data studies are possibly a response to the “hype of Big Data”, which assumes that massive datasets are the new means to ask and answer questions, without the necessity of e.g. theory. Critical Data Studies contest this view, by highlighting e.g. the non-neutrality of Big Data and the necessity to take the context of data collection and analysis into consideration.

More extensive summary: (had to laugh a bit bc it is a Google site 😉 )

Platform Studies:

Platform studies are not = Platform studies. One could either say that there are two streams or waves or that one evolved from the other, but there are certainly diverging definitions of platform studies out there. Deriving from New Media studies, specifically game studies, platform studies is often associated with Ian Bogost. Here, the prime focus was on the “platform” and how said platform affects the characteristics of the for instance application software, code etc. that is built upon them, or as Bogost and Montfort (2009) put it:

“Platform Studies investigates the relationships between the hardware and software design of computing systems and the creative works produced on those systems”.  Platform is a rather elusive term, as it is a relational concept, similar to infrastructure in the sociological stream of infrastructure. What is considered a platform depends on the individual, group or practice: “To someone who is porting Linux to the Dreamcast, the Dreamcast is the platform. To someone developing the scripting language Lua on Linux, Linux is the platform. To someone writing a program in Lua, Lua is the platform. It’s true, therefore, that platforms do not exist in the way that a hard drive or a binary file does.” (Bogost & Montfort, 2009).

Recently, platform studies have made a shift and have been extended to content-sharing websites and social media applications. With the advent of digital platform-based services, such as certain Google services and Facebook, the focus of platform studies has shifted to discuss how communication and expression are both enabled and constrained by new digital systems and new media. Key foci are programmability, affordances and constraints, the connection of heterogeneous actors, and accessibility of data and logic through application programming interfaces (APIs) (Plantin et al., 2018).

(New) Media Studies: New Media studies primarily explored the effects of digital technology on traditional media, looking at how new media are changing daily practices and cultures. In addition to this, New Media studies look at new forms of cultural representations, how they are created, consumed and shared and therefore focuses on analyzing e.g. cinema, news, adverts, games, movies, TV shows, looking at the content, history and effects of various media, particularly mass media. (from different theoretical standpoints, e.g. analyzing a movie applying a feminist perspective). New Media Studies is also connected to communication studies.

Code studies: As the name states, code studies focus on code rather than a program or software. Particularly, Critical Code Studies look at the politics and power embedded in code (potentially also algorithms) and how they possibly re-frame and re-shape practices, domains etc. IN that sense, computer code might serve as an entry point to explore digital cultures and opens up for hermeneutic approaches to the interpretation of code. Specifically in Critical Code Studies, code is are not considered value-neutral, similar to the perspective of Critical Data Studies view data as not neutral-free. In addition to this Critical Code Studies might feature some, in my opinion, rather linguist/semiotic perspectives. Code is viewed as an authoring tool, that is used to create, produce and consume (verbal) signs, ultimately creating artefacts. Critically exploring code is, therefore, necessary to understand the constraints and capabilities of said authoring tool as well as its contextual settings.

Algorithm Studies: In algorithm studies, algorithms are treated as social concerns. Algorithms are omnipresent (although hidden) and actively shape people’s daily interactions, practices, communication, briefly said; algorithms actively shape people’s daily lives. I am not sure what could be an example of critical algorithm studies could be, but I think, studies on loan approval algorithms can be included. However, the data that is used to train algorithms is usually criticized, instead of the algorithm itself, so I am not sure if they are counted as Critical Data Studies. On top of that, algorithms are sometimes incomprehensible by most computer professionals, so I am not sure to what extent a social science inspired approach to algorithm studies could dive into technical aspects. Following this line of thought, algorithm studies might be relatively superficial in a technical sense but instead, highlight the necessity to consider contextual and social aspects when discussing algorithms.

Bogost, I., & Montfort, N. (2009). Platform Studies: Frequently Questioned Answers. 7.

Dalton, C. M., & Thatcher, J. (2015). Inflated granularity: Spatial “Big Data” and geodemographics. Big Data & Society, 2(2), 205395171560114.

Plantin, J.-C., Lagoze, C., Edwards, P. N., & Sandvig, C. (2018). Infrastructure studies meet platform studies in the age of Google and Facebook. New Media & Society, 20(1), 293–310.

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