In this post, Dr Vassilis Galanos continues his exploration of metrics, its place in Higher Education, and the impact of the Research Excellence Framework on our work practices. This post is part 2 of 3, and belongs to the Hot Topic theme: Critical insights into contemporary issues in Higher Education.
In Part 1, I wrote about how the UK’s Research Excellence Framework (REF), claiming to measure research quality with some kind of objective precision, can foreground the development of digital machinery (such as Generative AI) that is adjustable to the REF’s objective (or better: objectifying) metrics. In this post, continuing the thread from part 1, I will connect the REF to the context of broader student and faculty numerical rankings. REF, that for many academics also stands for “Rigorously Established Fear”, often ends up fostering a competitive environment where volume trumps substance and impact is staged in wording but often not grounded in practice. As an example of this, as part of the Edinburgh Futures Institute’s Data Civics Observatory, I encountered the frustration of local communities in Edinburgh who complained about researchers using their underdeveloped neighbourhoods to justify their grant allocation, but disappeared upon the project’s end.
Niche or curiosity-driven disciplinary-questioning endeavours get side-lined while churned-out, quota-meeting research takes centre stage, especially in the context of academic-industry collaboration. Such collaboration is initially phrased as an attempt to open-up the world of Academia into the real world, but, in practice, it transforms Academia itself into a peculiar type of industry. This mirrors the rise of performance indicators in corporate bureaucracies, which seek to optimise efficiency at the expense of innovation and creativity.
This obsession with optimisation and efficiency further increases the distance between metric-driven reporting as just a symbol and as practical social change (as Matthew Archer recently showed in his 2024 book ‘Unsustainable: Measurement, Reporting, and the Limits of Corporate Sustainability,’ or, as Stanislav Andreski beautifully put it in 1970, “evasion in the guise of objectivity”; “quantification as camouflage’; and ‘techno-totemism and creeping crypto-totalitarianism”).
As an individual progresses up the academic ladder from student to staff, the REF exercise takes the emotional place occupied by the marker’s assessment and staff mentor’s supervision as the higher and sufficiently invisible entity of surveillance. This mirrors Marx’s description of a factory, which, in our case, is the university (my additions in square brackets):
“The technical subordination of the workman [read: worker, but also student, lecturer, professor, etc] to the uniform motion of the instruments of labour [including marking schemes, impact assessments, article production, grant allocation mechanisms], and the peculiar composition of the body of workpeople, consisting as it does of individuals of both sexes and of all ages, give rise to a barrack discipline, which is elaborated into a complete system in the factory [and academia], and which fully develops the before mentioned labour of overlooking, thereby dividing the workpeople into operatives and overlookers, into private soldiers and sergeants of an industrial army. […] The place of the slave-driver’s lash is taken by the overlooker’s book of penalties [including late submission penalties, resits, redundancy of academics who did not produce REFable outcomes, and more]” (Marx 2013: 293).
In the next, and final, post of this three-part series, I will conclude this conversation by situating the emergence of Generative Artificial Intelligence (GenAI) within the afore-described process of metrics-oriented culture.
Vasileios Galanos
Dr Vassilis Galanos, SFHEA is a visitor at the Edinburgh College of Art and works as Lecturer in Digital Work at the University of Stirling. Vassilis investigates historico-sociological underpinnings of AI and internet technologies, and how expertise and expectations are negotiated in these domains. Recent collaborations involved the history of AI at Edinburgh, interrogations of generative AI in journalism (BRAID UK), artist-data scientist interactions (The New Real), and community-led regeneration interfacing with data-driven innovation (Data Civics). Vassilis has co-founded the AI Ethics & Society research group and the History and Philosophy of Computing’s (HaPoC) Working Group on Data Sharing, also acting as Associate Editor of Technology Analysis and Strategic Management.
