The original proposition that sparked this discussion was put forth in a recent study by a couple of psychologists that claimed that “academic math-intensive science is not sexist”. This study has many methodological flaws, and I’m not sure that its rather sweepingly generalized conclusions are really justified.
We ended the last part of this commentary by noting that many if not most behavioral science model-builders seem to be afraid of making predictions that might prove them wrong. The only analysts who actually revel in predictions seem to be the sabermetricians (sports statisticians), perhaps because they can demonstrate real value to their analyses (cf. Moneyball.)
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Research is a lot about studying the characteristics of large things — large populations of people and organizations, big ideas and concepts, lots of time. But often large things aren’t all that easy to get at. More usually, the population is not available to us for a variety of practical reasons, and we only have access to pieces of it.
At one point in the CHE discussion on transferability of ideas across courses, a participant noted that students graduating from their program often expressed concern as to whether all they had taken added up to anything useful in the Real World. As conditions out there become increasingly difficult particularly for new graduates, this is a completely legitimate concern,
There’s an interesting discussion currently underway on one of the Chronicle of Higher Education blogs regarding the issue of students transferring skills and techniques learned in one class to another class. The original article and a lot of the comments support the idea that transferability tends to be pretty low, and that while faculty are sensitive to and generally try to enhance transferability,