HESA

Higher Education Strategy Associates

Research Grants by Discipline

So, tomorrow, HESA will be releasing its inaugural set of Canadian research rankings. We think they’re pretty cool; not only are they the first attempt in Canada to employ field-normalization techniques on bibliometric data, as far as we’re aware, they’re the first rankings anywhere in the world to employ field-normalization on research income.

Why does this matter? Well, not all research was created alike. Each discipline has a different publication culture, for starters. The average H-index score for an academic in astrophysics is about four times that of an environmental scientist and ten or eleven times that of a historian. Without field-normalization, any mediocre bunch of physicists trounces the best history department in the world. Yet, remarkably, most rankings and ratings systems choose to compare universities without normalizing for differences in publication culture.

It’s the same with research funding. Not only are researchers in some disciplines likelier to receive money than others, but the size of the average grant also differs because it’s inherently more expensive to run experiments in some disciplines than others. The gap between disciplines would be even greater if NSERC actually funded projects fully, but that’s another story.

A few months ago, we showed you some of the differences in disciplinary H-index averages, so you should already have a sense of how those differences play out. But we haven’t shown you the differences in funding by discipline. And so, herewith, the average amount of granting council funds distributed in 2010-11 per professor, by discipline, for selected disciplines.

Average Granting Council Awards per Faculty Member, by Discipline

There aren’t a lot of real surprises here: on average, the amount of funding per faculty member in engineering is about sixteen times what it is in the humanities and seven times what it is in the social sciences. It’s also about 40% more than it is in the sciences. This, of course, is the reason why one should field-normalize data; without it, schools with large engineering schools will tend to look good regardless of how good their scholars are in the rest of the university.

Anyways, all of this and more tomorrow in our all new rankings! (You know you love them, you naughty people.)

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