The following post is the full-length version of a piece that was published in edited form in Times Higher Education.
In their song ‘Life’s an Ocean’, which appeared on their 1995 album A Northern Soul, The Verve sang of imagining the future and waking up with a scream because they were buying some feelings from a vending machine. I wouldn’t say that I was quite waking with a scream, but we might imagine a future in which academic practices have reached a similar state of efficiency, ordering and control. Put starkly, the day cannot be far away when there is an ‘app’ that tells us what articles to read. I’m imagining a simple application that builds up a personalized profile about the research articles we read, and that then uses that profile to predict what we are likely to want to read. Such devices are everywhere around us, informing us what music to listen to, what films to watch and what books to buy, so it can’t be long before such devices are doing our research for us. This will be the day when, as Donna Haraway predicted all that time ago, we will become more inert and our research devices will become more lively.
Imagine the ease of researching in a world where the research materials ‘find us’. Where we need only log in to see what we must read in order to complete a research project. No more searching around, no more wasted time reading the wrong things or looking in the wrong places, no more aimless flâneur wandering around libraries or flicking through e-journals to see what we might find. None of this will be needed because the power of algorithms, as Scott Lash has put it, will be reshaping the academy. These algorithms will be streamlining, making efficient, predicting, making decisions for us, doing work on our behalf, taking some of the agency from researchers and the research processes and making it their own. This is Nigel Thrift’s ‘knowing capitalism’ playing out in the academy.
This sounds extreme but these things are not going to part of a sudden rupture or a grand explosion of social change in higher education. Algorithms are already sorting out the academy in lots of ways, many of which we have little awareness of. I’ve opened this piece with something that might seem like futurism, but the reality is that software algorithms, as with many sectors of the social world, are already shaping practice as they become integrated into ordinary everyday processes of research, teaching and administration.
I’ve been quite speculative in suggesting that research articles will come to find their readers, but in many ways this is already the case with books. We need only to think here of how the now famous predictive algorithms of Amazon are already quite powerful in shaping our encounters with academic books. Here our profiles, the things we have purchased, are used, along with data about other people’s purchasing practices, to predict what books we might be interested in buying. We might question their accuracy in predicting, but these systems nevertheless shape our literary encounters.
Amazon’s algorithmic recommendation system is by now quite familiar, but have we thought about how this might already be shaping research outcomes. It is likely that we have all had moments where the recommendation system on Amazon has made a suggested purchase which we have then gone on to buy, read and build into an article, book or even a lecture. Clearly here these algorithmic processes have implications for the way that our research or teaching turns out. But this is just one visible moment at which the algorithmic processes are more obvious. In Rob Kitchin and Martin Dodge’s (2011) recently published book Code/Space: Software and Everyday Life, the authors demonstrate the importance of software for the functioning of the social world – ranging across sectors from the home to air travel. It would be remiss to think that higher education somehow sits outside of these broader social developments. In the book Kitchin and Dodge point out that even some very mundane technologies like Microsoft Word or Adobe Photoshop come ‘loaded’ with ‘algorithmic normalities’ that ‘subtly…direct users to certain solutions’. Imagine mapping such an observation onto academic practices. Without thinking too hard we could immediately add that Powerpoint’s algorithmic normalities are likely to be providing us with subtle directions in how to lecture (a point the comes up frequently yet indirectly in discussions about how this particular software package has become so dominant in presentation and lecture formats).
Elsewhere the power of algorithms might be more pronounced but perhaps less acknowledged. In research in particular we might not have yet reached the point where our research is performed for us by algorithms, at least not to the extent that our background reading ‘finds us’ perhaps, but algorithms are nonetheless implicit in our research practices. We can begin by thinking about how powerful Google’s famous PageRank algorithm is in shaping knowledge by influencing what we find, encounter and learn about – this algorithm has recently been beautifully described by John MacCormick (2012) in his book 9 Algorithms that Changed the Future. Google is an interface that almost inevitably plays a part in social research, as we search around for other researchers in our fields, as we look for background information, as we check on a speaker we spotted talking at a conference and perhaps even when we unwittingly discover something that triggers an idea for a project. We can of course add to this the growing use of Google Scholar as a means of finding materials and readings, which, again, is likely to be shaped by the ranking of the relevance of the articles it locates within our search terms. Some of us might also be following news feeds about our networks on places like academia.edu, and in turn drawing upon such information to guide our reading.
Algorithmic processes are also now an implicit part of the research itself as software become a part of analytical processes. In research across the social sciences and humanities many forms of software now perform the task of sorting out data in various ways. I was recently trained in the use of SPAD for doing Multiple Correspondence Analysis, here the algorithms perform the complex mathematics that enable survey data to be mapped on to geometric space, thus revealing all sorts of apparent cultural oppositions and tensions. Elsewhere the analytical capacities of SPSS are a widely used resource for quantitative analysis. Atlas.ti’s algorithmic functions are used to perform pattern recognition on qualitative interview data sets, to group the content and to see what the data are saying. The list goes on, we could add for example the sorting and searching functions of NVivo or LexisNexis. All of these incorporate some form of algorithmic process in the analysis of the data. These algorithmically aided analyses feed into findings, shaping knowledge and then perhaps playing out in material ways through policy, planning and the like.
And of course, all of this is before we even begin to think about how algorithmic processes converge with higher education’s systems of measurement in the distribution of funding, the production of league tables, the allocation of resources, the outcomes of research excellence frameworks, Key information set widgets, the use of citations, the ordering and ranking of journals and so on.
The outcome is that we need to, as the geographer Stephen Graham has put it, look into the ‘very guts’ of these systems. We need to see these algorithms as powerful and largely unacknowledged agents in academia. Researchers are now looking at the social power of algorithms in various sectors including the financial sector, the military, and in bioinformatics, but at the moment we are leaving out the higher education sector. It is important for us to begin to acknowledge and think through the power of algorithms as they come to order and shape our research and teaching practices, and as they come to play a part in the formation and communication of knowledge. At the moment we are paying little attention to these developments. As a consequence we are being reworked from the inside-out with little reflection. The possibility of our research materials ‘finding us’ is temptingly convenient, but it is a future in which the academic practices of the past will have been quite drastically reworked. I’m not saying that we should automatically resist such processes, this would not really be feasible given that they are already deeply embedded in routine academic practice, but I am suggesting that there is a need to develop some further understanding of the power of algorithms in the performance of academic life.