Hard and soft science — a flawed dichotomy

11 July, 2018 at 19:08 | Posted in Theory of Science & Methodology | 1 Comment

The distinctions between hard and soft sciences are part of our culture … But the important distinction is really not between the hard and the soft sciences. Rather, it is between the hard and the easy sciences. Easy-to-do science is what those in physics, chemistry, geology, and some other fields do. Hard-to-do science is what the social scientists do and, in particular, it is what we educational researchers do. In my estimation, we have the hardest-to-do science of them all! We do our science under conditions that physical scientists find intolerable. We face particular problems and must deal with local conditions that limit generalizations and theory building-problems that are different from those faced by the easier-to-do sciences …

Context-MAtters_Blog_Chip_180321_093400Huge context effects cause scientists great trouble in trying to understand school life … A science that must always be sure the myriad particulars are well understood is harder to build than a science that can focus on the regularities of nature across contexts …

Doing science and implementing scientific findings are so difficult in education because humans in schools are embedded in complex and changing networks of social interaction. The participants in those networks have variable power to affect each other from day to day, and the ordinary events of life (a sick child, a messy divorce, a passionate love affair, migraine headaches, hot flashes, a birthday party, alcohol abuse, a new principal, a new child in the classroom, rain that keeps the children from a recess outside the school building) all affect doing science in school settings by limiting the generalizability of educational research findings. Compared to designing bridges and circuits or splitting either atoms or genes, the science to help change schools and classrooms is harder to do because context cannot be controlled.

David Berliner

Amen!

When applying deductivist thinking to economics, mainstream economists set up their easy-to-do  ‘as if’ models based on a set of tight axiomatic assumptions from which consistent and precise inferences are made. The beauty of this procedure is, of course, that if the axiomatic premises are true, the conclusions necessarily follow. The snag is that if the models are to be real-world relevant, we also have to argue that their precision and rigour still holds when they are applied to real-world situations. They often do not, and one of the main reasons for that is that context matters. When addressing real-world systems, the idealizations and abstractions necessary for the deductivist machinery to work simply do not hold.

If the real world is fuzzy, vague and indeterminate, then why should our models build upon a desire to describe it as precise and predictable? The logic of idealization is a marvellous tool in an easy-to-do science like physics, but a poor guide for action in real-world systems in which concepts and entities are without clear boundaries and continually interact and overlap.

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1 Comment

  1. I would follow Keynes in supposing that there is no such thing as a universal scientific method that one can trust. One needs to treat ‘method M is adequate in this context’ as a hypothesis, ‘H’, and (implicitly) ‘the context is sufficiently stable for H to be tested’ as a more fundamental hypothesis.

    By ‘hard’ sciences we generally mean areas in which these hypotheses have been well tested and not falsified, with failings in a widely-held H leading to occasional revolutions rather than denials.

    We could build on Keynes and use the term ‘pseudo-science’ to describe activity that superficially resembles science but which takes H for granted or even professes H despite strong evidence to the contrary.

    The term ‘soft’ science seems to be used for science which is not hard. But it seems to me that there is a lot of good soft science that is not pseudo. These could be an exemplar for the development of ‘proper’ economics. (Hard work, though.)

    In contrast to your last para, my own view is that would be scope for education to apply Takens’ embedding theorem, for example, but there might be better routes to the same end. Regards.


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