There are strong claims that it is becoming harder for individuals trying to build an academic career to maintain a unitary academic identity for themselves. Explanations for this include the marketisation of higher education and the plethora of job roles, demanding 'flexibility' in working patterns. I use the opportunity to present an individual paper and take the audience through stages in conceptual analysis to explain how the ground for the fragmentary identity goes deeper, down to our understanding of academic practice (...) as a concept. Standard moves in response to the problem of definition can reveal only so much. I look to theories about artistic and scientific practice to allow us to attain more enlightenment about trying to define academic practice and also to supply the ground for autonomy for practitioners, as freedom competently to direct their own work. (shrink)

Objective Bayesian epistemology invokes three norms: the strengths of our beliefs should be probabilities, they should be calibrated to our evidence of physical probabilities, and they should otherwise equivocate sufficiently between the basic propositions that we can express. The three norms are sometimes explicated by appealing to the maximum entropy principle, which says that a belief function should be a probability function, from all those that are calibrated to evidence, that has maximum entropy. However, the three norms of objective Bayesianism (...) are usually justified in different ways. In this paper we show that the three norms can all be subsumed under a single justification in terms of minimising worst-case expected loss. This, in turn, is equivalent to maximising a generalised notion of entropy. We suggest that requiring language invariance, in addition to minimising worst-case expected loss, motivates maximisation of standard entropy as opposed to maximisation of other instances of generalised entropy. Our argument also provides a qualified justification for updating degrees of belief by Bayesian conditionalisation. However, conditional probabilities play a less central part in the objective Bayesian account than they do under the subjective view of Bayesianism, leading to a reduced role for Bayes’ Theorem. (shrink)