Quality Assurance is Not Morally Neutral
At Edinburgh, School Quality Assurance and Enhancement Committees report into College Quality Assurance and Enhancement Committees who report into Senate Quality Assurance and Enhancement Committees. We all follow the Academic Services guidance on quality assurance which includes the UK Quality Code for Higher Education and of course QAA Scotland’s Quality Assurance and Enhancement Framework. That is a lot of reporting and guidance frameworks to look at. And a lot of advice into what Quality Assurance is.
But I am also interested in what it is not. I’ve been having meetings with colleagues in the run up to my Chair takeover, and we’re all agreed we don’t want more ‘checkbox’ type exercises. QA is a process which involves labour, and its probably not well recognised in our workloads. Worthington and Hodgson delightfully talk about strategies to resist quality assurance labour: devolving, shirking, dithering and deceiving. And who among us can say we haven’t done this? Personally I’m partial to the old shirk and devolve. Throw a big idea out and then wander off to do something else.
To do quality assurance meaningfully, I think we need to be careful about what work we impose, and to try and think strategically about what data already exists. I want to incorporate more Open Science practices to Quality Assurance. I’ve actually already talked about this elsewhere, but I see these as really sympathetic causes.
Part of this is thinking about what we record and why, and having shareable processes to minimise labour where we can. I fundamentally view Open Science as having a strong moral component, one which we admittedly don’t always recognise the labour cost of. And that brings me to the thoughts I’m having about Quality Assurance.
The fact that QA is not morally neutral is probably not a surprise to most of us, but I think its worth stating outright. The basic principle being that what we choose to measure and report on, and what we don’t, describes what we value. For me, one of the most convincing theses on this is Alex Cobham’s The Uncounted. Thinking about what you choose to measure, and how you choose to report on it, can empower you to make decisions. A lack of thought on what you choose to measure and report on will most likely simply protect the status quo. I found this paragraph from Bohman et al interesting.
Feminists have shown how supposedly neutral or impartial norms have built-in biases that limit their putatively universal character with respect to race, gender, and disability (Mills 1997; Minnow 1990, Young 2002). In all these cases, claims to scientific objectivity or moral neutrality are exposed by showing how they fail to pass the test of public verification by showing how the contours of their experiences do not fit the self-understanding of institutional standards of justice (Mills 1997; Mansbridge 1991). Such criticism requires holding both one’s own experience and the normative self-understanding of the tradition or institution together at the same time, in order to expose bias or cognitive dissonance.
I hope that as Chair of QA, one of the things I’ll be able to help out with is making good choices about what we measure and why. And I’d be really interested in anyone’s thoughts on this.