Being a jackass of n>1 trade is the lot of the roaming physicist who has a glimmer of self-awareness, and one regularly finds oneself exploring texts and topics in a way that undergraduate students of that discipline would find shallow. Last year, Karl Popper ruined my summer when I tried to read his Logic of Scientific Discovery. I’ve previously enjoyed The Open Society and its Enemies, but I didn’t think Logic was very good, and nor did I feel I understood it enough to really get anything out of the experience. I thought that might be it for me and philosophy of science. Luckily, a conversation with Oliver Marsh from the UCL STS department earlier this year at a Science Showoff prodded me to have a go at reading Thomas Kuhn’s The Structure of Scientific Revolutions*. “Kuhn loves science”, Mr Marsh enthused, “he even used to be a theoretical physicist!”. I was sold.
The Structure of Scientific Revolutions is pretty good. For a start, it’s readable for nonspecialists like me, and accessible. I would say that (like Popper) he makes his point fairly early on and then spends way too long parameterising and paradigmising it, but this is intended to be something like an extended paper rather than a popular science history book; it’s not entirely aimed at idiots like me. His central focus is on the paradigm – now an idea so ubiquitous that it’s pretty much common parlance, but he uses it in various ways which I will choose to sum up as “a bunch of shared ideas and values that people working in a discipline hold about the way the world works and what is interesting to study”. In his conception, “normal science” proceeds when this set of shared ideas are held, used, prodded, tested, and sometimes, found to be wanting – which precipitates a tumultuous “paradigm shift”. This description of what scientists spend their time doing, informed by, one presumes, his experience, as as well as historical literature, rang true to me**.
Kuhn describes the practice of normal science as like a jigsaw puzzle – people using their ingenuity to get all of the pieces in place, and creating something at once satisfying and beautiful. Maybe it’s a jigsaw of a really awesome painting. You might be really good at jigsaws but not a very good painter. Actually, the metaphor that struck me as even more apposite was that of fanfiction – the phenomenon of people using their favourite characters from books and films, and writing new stories around them (I don’t think this was as popular when Kuhn was writing, or maybe he’d have thought of it himself). Where else do smart minds take other people’s ideas and tropes, and then exert their creativity to test them to destruction on the rocks of plausibility? Fanfic writers do some pretty… risqué stuff with their favourite characters, but they are constrained by the canon that they are inspired by and hold so dear. Edward and Bella outgrow the roles written for them, and you have Dorian Gray or David Gray or whatever he’s called, and bingo! Paradigm shift.
Ok, scientists test the canon of their subject’s mainstream on esoteric experiments and theories, to destruction on the rocks of nature. And in science, the source material is really good***, and a lot of the fanfic is really good. But laboratory physicists are exactly the sort of people I expect to write fanfic. Or, to return to Kuhn’s original formulation, would you rather paint a picture or do a jigsaw puzzle? Maybe Kuhn’s conception is a false dichotomy; constraints breed creativity, and even artists have limits set by their media. Even within paradigms, there are shades of grey.
As a (relatively) newly-minted UCL lecturer, I’ve been working to complete my teaching qualification at the Institute of Education – a “Professional Certificate” in higher and professional education. It’s given me a lot of opportunities to reflect on my Brownian academic motion, and how this informs where I am now as a teacher, but also a researcher (short version: physics undergrad, solid state physics/materials science doctorate, 4 years as a medical physicist, and now 3 as a social physicist). One of the more interesting elements for me was one that came out in passing during a session led by Holly Smith – the idea of “high-consensus” and “low-consensus” subjects.
The definition is sort of what you’d imagine: high-consensus subjects agree on a lot of stuff and low-consensus ones don’t. Holly pointed me to one of the original papers on the subject – an article from 1973 where researcher Anthony Biglan carried out a study at the university of Illinois, and in parallel, a smaller liberal arts college.
Distributing cards with subject names on, he asked academics to rank each subject in terms of how “close” it is to their subject. He then used a bit of clever maths magic called “Multidimensional Scaling”, or MDS. MDS is clever in that it takes a series of distances (in this case, the “distances” between subjects) and assumes that this is due to points representing each subject “floating around” in some abstract space. Using the distances between them, it “triangulates” where these subjects were in the space.
To think of a more concrete example, imagine if you don’t know where the London tube stations are but you know the distances between stations. MDS would let you work out the configuration of stations in space based on just those distances†. In Biglan’s case, he was reconstructing positions in a sort of abstract space, and rather than two, he used three dimensions, which he found to correspond to the distinctions “hard/soft”, “pure/applied” and “life/non-life”. In this classification Physics would be “hard/pure/non-life”; Management might be”soft/applied/life” and so on. But as in space, there are potentially a continuum values, not just binary yes/no answers.
It’s interesting that these dimensions pop out. In the follow-on literature I’ve read, people talk about “hard/soft” distinctions, or even “paradigmatic” subjects (after Kuhn), but my favourite is “high/low consensus” – in other words, high consensus subjects are those in which there is a lot of agreement about the methods we use, the technology we employ, and/or a shared body of facts – which does sound very Kuhn-like. I like this description because it’s quite person-focussed (“how much do we agree?”), it sums up the essential features, echoes the way these subjects are taught (or have been taught), and doesn’t have the weird macho baggage of studying a “hard science” vs a “soft subject”.
Ultimately, I’m interested in this distinction because I’ve moved from some very high-consensus areas to some lower-consensus ones; and because adapting my thinking and research and teaching styles hasn’t necessarily been that easy. What my PhD taught me, though, was even in a high-consensus subject, creativity, critical thinking, and the ability to deal with uncertainty is necessary to be a dynamic researcher. Thinking about ways to make high-consensus subjects more low-consensus is interesting to me, because it’s arguably that kind of thinking that opens up academia to public participation and engagement, and opens up teaching to be a more valuable process for the teacher. There are plenty who would say it makes it a more valuable process for the learner too. But taking teaching and research from a high-consensus world into a low-consensus one can be challenging, even for serial interdisciplinarians like me.
*I know it’s 50 years old. I am behind on my reading list.
**Of course, I should always be a bit wary of reading too many things that simply remind me of things I’d forgotten I believed
***better than Twilight, even
†actually, you’d also need the absolute positions of two stations to get the absolute location and correct n/s/e/w orientation right, but everything else would be ok.