Carrie Figdor

I separate two intrinsic/extrinsic distinctions that are often conflated: one between properties (the intrinsic/extrinsic, or I/E, distinction) and one between the ways in which properties are had by individuals (the intrinsically/extrinsically, or I-ly/E-ly, distinction). I propose an analysis of the I-ly/E-ly distinction and its relation to the I/E distinction that explains, inter alia, the puzzle of cross-classification: how it can be, for example, that the property of being square can be classified as an intrinsic property and yet individuals can be square intrinsically or else extrinsically

In this essay I raise a dilemma for science journalists based on recent skepticism raised by scientists about the credibility of published results in many fields. Due to systematic biases in the publication record, most published findings in these fields (including psychology and biological subfields) are almost certainly false. So should science reporters stop reporting these findings, given their mission to report verified truths? Or should they report the findings while saying they are almost certainly false?

Work on the intrinsic/extrinsic distinction is often motivated by its use in other areas, such as intrinsic value, real vs. Cambridge change, supervenience and other topics. With the exception of Figdor 2008, philosophers have sought to articulate a global distinction -- a distinction between kinds of properties, rather than ways in which individuals have properties. I argue that global I/E distinctions are unable to do the work that allegedly motivates them, focusing on the case of intrinsic value.

I review a widely accepted argument to the conclusion that the contents of our beliefs, desires and other mental states cannot be causally efficacious in a classical computational model of the mind. I reply that this argument rests essentially on an assumption about the nature of neural structure that we have no good scientific reason to accept. I conclude that computationalism is compatible with wide semantic causal efficacy, and suggest how the computational model might be modified to accommodate this possibility.

This chapter discusses the nature of objective news and the debate regarding its possibility. It then assesses the main arguments for the unattainability of objective news. A close examination of these arguments shows that, contrary to widespread belief, journalists who try to provide objective news are not striving in vain. The chapter discusses the effect of competing journalistic aims and other limitations on our efforts to generate objective news. It suggests that the unwarranted skepticism regarding the possibility of objective news is an artifact of the changing priorities of journalists and inadequate journalistic methods, and that the only real issue is how we can better train those journalists who want to generate objective news.