ONE. Performance information: signalisation and dressage
Data is the bleeding edge. Follow the data to see where education is being cracked for value. Follow the data to see who is doing the cracking. Follow the data to see who is engaged in this process of enforced, public and open, educational data production. Follow the data to see who is then enclosing and commodifying that open and public data for profit. Follow the data to see who is selling and re-selling new services back into open and public spaces, and charging rents for them. Follow the data to see the transnational networks of dispossession that are using secondary policy, processes of entrepreneurialism, debt and indentured study, financialisation, and the assault on labour rights, to lever value.
And I am reminded of all this because Martin Eve pointed me to this University of Nottingham video on performance information. Not learning analytics. Not management information, but performance information. The disciplining of academic labour, where that labour is the work of both staff and students. Sold back to us as what students want, because their expectations have changed. Sold back to us in terms of progression and retention. Sold back to us as the new-normal.
Performance information sold back to us. The new normal. Dashboarding for success. For a moment I forgot myself and I read that as “waterboarding [academic labour] for success”.
TWO. I remember…
And I remember that I have thought about this in terms of student debt, big data and academic alienation, arguing that “the mechanisms by which established hierarchies maintain their power through financialisation and information-sharing need to be described, and alternative positions developed.”
And I remember that I have thought about this in terms of globalisation and the University, arguing that “the key is to understand how technology-driven innovations relate to the globally-hegemonic fraction of transnational, finance capital. This is critical because these innovations are not outside the circuits or cycles of globally mobile capital. Thus, these innovations further reduce the technical constraints or barriers to the reproduction of capital and its valorisation/accumulation processes, just as they revolutionise the transportation, interaction, production and consumption of individuals with (intellectual or cognitive) commodities/products.”
And I remember that I have thought about this in terms of circuits of affect and resistance, arguing that “social relations are increasingly structured by technically-mediated organisations like schools and the University, which then re-inscribe socio-political hierarchies that are increasingly technological, coercive and exploitative. This coercive and exploitative set of characteristics is driven by the competitive dynamics of capitalism, and especially the ways in which the socially necessary character of the labour-power expended in producing a particular commodity or innovation or technology is diminished over-time. This reduces the value of knowledge and specific immaterial skills in the market, resulting in a persistent demand to innovate, to become entrepreneurial or to hold and manage proprietary or creative skills.
And I remember that I have thought about this in terms of the domination of time and the liberation of a pedagogical alliance, arguing that “flows of management information like psychometric test outcomes and workload data, performance metrics like retention and progression data, and enriched use of technologies to manage research and teaching, attempt to reduce all academic activities to flows that take place in real-time, through structures that are always-on, with feedback and inputs that are “just in time”. As a result the University, like any other capitalist business, attempts to abolish time. Technologies and techniques are designed to accelerate production, to remove labour-related barriers, and to destroy the friction of circulation time.”
And I remember that I have thought about this in terms of money, labour and academic co-operation, arguing that “This is a clear manifestation of the subsumption of academic research, in particular about progression into higher education and about pedagogic practice, for policy that is based on re-engineering society for market principles. Whilst networks exist (here from policy maker to think-tank) to promote those privatised principles in spaces that were/are publically-regulated, funded and governed, a critical question is whether it is possible to nurture networks that push-back against this hegemonic position? ”
And I remember that I have thought about this in terms of research and the circuit of impact, arguing that “Inside the University, impact signals compulsion that is itself self-harming behaviour, and then enforces dressage in the name of power. This point was made at Governing Academic Life by Michael Power, in his focus on the role of impact in acting as a form of governance over academic labour. He argued that impact was an open and public closure of what can be discussed and produced, in order that a governance/command structure for value production could be imposed. Here metrics and investment interact to forms a circuit of capital rooted in academic production, with that productive power of research being disciplined through signalisation that then imposes a form of dressage… we are witnessing the attempt by finance and commercial capital to synchronise production with their own circuits. This is an uncomfortable symbiosis, as those of us engaged in a higher education that is being restructured by the dictates of finance capital and a new market can attest.”
And I remember that I have thought about this in terms of the proletarianisation of the University, arguing that “This is the relationship between labour-power and subsumption/accumulation across areas of work that were previously regarded as beyond the market. What is revealed in this process is the dispossession of individual and collective autonomy and time. The autonomy that is dispossessed relates to what can be produced and the process of production. The time that is dispossessed is both the present and the future that is foreclosed as it is alienated. This alienated labour-power is scrubbed clean of its usefulness beyond that dictated in the market by metrics, impact and satisfaction. What emerges is the substitution of that alienated labour-power for that which was previously locally-bargained, with control over the means of production residing transnationally rather than at a local level.”
THREE. What a mess.
This matters because we are now being taught about innovation spillovers by HM Government, and the explicit value of education to the wider economy. We are told that:
the share of hours worked by highly skilled employees is positively linked to almost all of the measures of productivity, profits and trade performance. Expenditure on training is associated with increased labour productivity at the enterprise level. Purchases of goods and/or services from the Education sector (comprising schools, and further and higher education institutions) increases labour productivity at the sector level, total factor productivity at both the sector and enterprise level, and the ratio of exports to output at the sector level. Exposure to spillovers from education purchases is negatively correlated with labour productivity but positively and significantly correlated with all the other performance variables. (p. 15)
And we already know that Universities UK are driving data-driven change in the name of a smarter, stronger sector.
And we already know of the work around Britain’s “Emergent Corporate Universities”: Academia in the Service of International Capital and the Military Industrial Complex.
FOUR. Data and anxiety: does it really have to be this way?
In her excellent essay on the anxieties of big data, Kate Crawford argues:
Already, the lived reality of big data is suffused with a kind ofsurveillant anxiety — the fear that all the data we are shedding every day is too revealing of our intimate selves but may also misrepresent us. Like a fluorescent light in a dark corridor, it can both show too much and not enough. Anxiety, as Sianne Ngai has written, has a temporality that is future oriented: it is an expectation emotion, and the expectation is generally of risk, exposure, and failure. British group Plan C in their blistering manifesto “We Are All Very Anxious” argue that anxiety is the dominant affect of our current phase of capitalism, engendering political hopelessness, insecurity, and social separation.
The current mythology of big data is that with more data comes greater accuracy and truth. This epistemological position is so seductive that many industries, from advertising to automobile manufacturing, are repositioning themselves for massive data gathering. The myth and the tools, as Donna Haraway once observed, mutually constitute each other, and the instruments of data gathering and analysis, too, act as agents that shape the social world. Bruno Latour put it this way: “Change the instruments, and you will change the entire social theory that goes with them.” The turn to big data is a political and cultural turn, and we are just beginning to see its scope.
Overcoming anxiety with anonymity then becomes the thing, as Tiqqun argue. This is the very ability to define a subjectivity beyond the hegemonic control of data as experience:
Establishing a zone of opacity where people can circulate and experiment freely without bringing in the Empire’s information flows, means producing “anonymous singularities,” recreating the conditions for a possible experience, an experience which will not be immediately flattened out by a binary machine assigning a meaning/direction to it, a dense experience that can transform desires and the moments where they manifest themselves into something beyond desire, into a narrative, into a filled-out body.
In her outstanding Ph.D. thesis on “The State Machine : politics, ideology, and computation in Chile, 1964-1973”, Jessica Miller Medina highlighted how the Allende Government in Chile attempted to utilize technology and data (through cybernetics) to create a new representation of society beyond the market, using different, co-operative organizing principles. The key for Miller Medina was to describe
not just a technological history but a history of the changing social networks that connected these technologies to the function of the state and its management (p. 17).
Moreover, her work reminds us to see the technological and technocratic ideas of Gartner and Willetts as means to “solidify a particular articulation of the state that was supported by new claims to legitimate power” (p. 96). Thus, she quotes Allende (p. 252) arguing for democratic renewal:
We set out courageously to build our own [cybernetic] system in our own spirit. What you will hear about today is revolutionary – not simply because this is the first time it has been done anywhere in the world. It is revolutionary because we are making a deliberate effort to hand to the people the power that science commands, in a form in which the people can themselves use it.
To use data beyond the market and beyond financialisation. To use data for co-operative performance beyond the market and beyond financialisation. To resist the co-option of data for impact and performance management. If you work in UK HE, good luck with that.