As neuroscience has gained an increased ability to enchant the general public, it has become more and more common to appeal to it as an authority on a wide variety of questions about how humans do and should act. This is especially apparent with the question of gender roles. The term ‘neurosexism’ has been coined to describe the phenomenon of using neuroscientific practices and results to promote sexist conclusions; its feminist response is called ‘neurofeminism’. Here, our aim is to survey (...) the phenomena of neurosexism and neurofeminism using a largely philosophical approach, incorporating concepts from the philosophy of mind, the philosophy of science, ethics, and feminist philosophy. First, we delineate how neuroscientific studies purporting to show sex brain differences may be prone to bias at a number of methodological levels – including the choice of categories to be studied, and the choice of tools for data gathering, analysis, and presentation. Then, we show how interpretations of such studies may wrongly assume the notion of ‘hard-wiring’. Furthermore, lack of attention to distinctions within philosophy of mind may result in a mistaken supposition that brain differences lead to mental and/or psychological and/or behavioral ones. It is not difficult to see how these forms of neurosexism, leading to claims of ‘hard-wired’ gender differences that map onto traditional and harmful gender stereotypes, raise ethical questions. We conclude by briefly considering one: are the harms caused by neurosexist studies and their interpretations outweighed by their potential benefits? (shrink)

Debate about the epistemic prowess of historical science has focused on local underdetermination problems generated by a lack of historical data; the prevalence of information loss over geological time, and the capacities of scientists to mitigate it. Drawing on Leonelli’s recent distinction between ‘phenomena-time’ and ‘data-time’ I argue that factors like data generation, curation and management significantly complexifies and undermines this: underdetermination is a bad way of framing the challenges historical scientists face. In doing so, I identify circumstances of epistemic (...) scarcity where underdetermination problems are particularly salient, and discuss cases where legacy data—data generated using differing technologies and systems of practice—are drawn upon to overcome underdetermination. This suggests that one source of overcoming underdetermination is our knowledge of science’s past. Further, data-time makes agnostic positions about the epistemic fortunes of scientists working under epistemic scarcity more plausible. But agnosticism seems to leave philosophers without much normative grip. So, I sketch an alternative approach: focusing on the strategies scientists adopt to maximize their epistemic power in light of the resources available to them. (shrink)

In this article, I analyze Quine’s conception of science, which is a radical defence of extensionalism on the grounds that first‐order logic is the most adequate logic for science. I examine some criticisms addressed to it, which show the role of modalities and probabilities in science and argue that Quine’s treatment of probability minimizes the intensional character of scientific language and methods by considering that probability is extensionalizable. But this extensionalizing leads to untenable results in some cases and is not (...) consistent with the fact that Quine himself admits confirmation which includes probability. Quine’s extensionalism does not account for this fact and then seems unrealistic, even if science ought to be extensional in so far as it is descriptive and mathematically expressible. (shrink)

Proponents of the value ladenness of science rely primarily on arguments from underdetermination or inductive risk, which share the premise that we should only consider values where the evidence runs out or leaves uncertainty; they adopt a criterion of lexical priority of evidence over values. The motivation behind lexical priority is to avoid reaching conclusions on the basis of wishful thinking rather than good evidence. This is a real concern, however, that giving lexical priority to evidential considerations over values is (...) a mistake and unnecessary for avoiding the wishful thinking. Values have a deeper role to play in science. (shrink)