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Flickr, Creative Commons, wwarby

Flickr, Creative Commons, wwarby

Athene Donald and her colleagues at Cambridge have raised some vitally important issues this week, not just for women, but for all of us who work in higher education. This is not, before I begin, another of those misery-lit pieces that seem to be a major genre of university blogging and book publishing these days. Nor is it a delusional, romanticised hankering to the past. Rather, it’s an attempt to raise the fundamental question of how we can build a higher education system that is successful, sustainable and fair. It’s a reminder too, to those enamoured by numbers and spreadsheets, that these are only ‘symbolic representations’ of the world, not a snapshot of the messy reality in which most of us live and work.

The technology that has best served fiction is not Word, but rather, Excel
‘What is it about boys and numbers?’ asks Susan in Gregory’s Girl. Good question. From football statistics to acceleration and horsepower of car engines (or even “the air-speed velocity of an unladen swallow”), Top Trump-ery has long been a hallmark of boyhood. In that cultural context perhaps it’s unsurprising that it often carries over, particularly in this numeracy driven financial-electronic era, to later in life. Of course, we all can appreciate the value of useful measures, of data, and of the use of evidence to inform decisions.

As an ex-scientist, I too am not bad at the old counting and number-juggling, as well as being highly appreciative of the power of advanced mathematical and computational techniques. But as an observational cosmologist, I was well aware of the distinction between elegant theoretical models and the lumpy, clumpy, asymmetric messiness of reality. Models are representations, simplifications, simulations and open to testing and challenge when they meet the real world replete with its ambiguities and complexity. Remember the old physics joke about this?

So now we’re in the era of KPIs and metrics, but have we crossed over from using such to inform policy and strategy to using policy and strategy to meet demands of the numbers? League tables, comparator charts (and the lovely looking new spider diagrams we all use in Ireland to compare each other), citations, h-factors, i-factors, grant proposal success rate, patents applied for and awarded, numbers of successful PhDs — It won’t be long before we’re onto the derivatives markets, looking at indicators of rate of growth of output, comparing statistics of ‘rising stars’ across institutions and trading our Trump Cards with one another using Monopoly money.

But as the spreadsheet model of the university grows its numbers of columns and its range of data visualisations, so it begins not to represent but to decouple from what those of us outside the inner sanctum actually experience. We haven’t the time to stare at the columns of data and look for a tweak here, a tweak there. Nor do we stare at the league tables willing ourselves up a few notches, praying and hoping — have we got the measures right? Have others finessed their results better than we have ours? The pacing, the day before, the preparing of the Press Release to cry institutional triumph or blame lack of government support: press 1 to send PR statement A or 2 to send statement B.

And meanwhile, month after month, as data accumulates, the institution, its people, processes and culture are hammered into a new shape, to better fit the model. A model that excludes the human and the cultural cost to students, to academics, to the (increasingly outsourced and underpaid) support staff, to wider society and to the very disciplines themselves.

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