[First published 09/2016; substantive revision 02/2021.] Evaluative terms and concepts are often divided into “thin” and “thick”. We don’t evaluate actions and persons merely as good or bad, or right or wrong, but also as kind, courageous, tactful, selfish, boorish, and cruel. The latter evaluative concepts are "descriptively thick": their application somehow involves both evaluation and a substantial amount of non-evaluative description. This article surveys various attempts to answer four fundamental questions about thick terms and concepts. (1) A “combination question”: (...) how exactly do thick terms and concepts relate evaluation and non-evaluative description? (2) A “location question”: is evaluation somehow inherent to thick terms and concepts, such as perhaps an aspect of their meaning, or merely a feature of their use? (3) A “delineation question”: how do thick terms differ from the thin and from other kinds of evaluative terms? (4) Given answers to these questions, what broader philosophical significance and applications might thick concepts have? (shrink)

In conversation, speakers often felicitously underspecify the content of their speech acts, leaving audiences uncertain about what they mean. This paper discusses how such underspecification and the resulting uncertainty can be used deliberately, and manipulatively, to achieve a range of noncommunicative conversational goals—including minimizing conversational conflict, manufacturing acceptance or perceived agreement, and gaining or bolstering status. I argue that speakers who manipulatively underspecify their speech acts in this way are engaged in a mock speech act that I call _pied piping_. (...) In pied piping, a speaker leaves open a range of interpretations for a speech act while preserving both plausible deniability and plausible assertability; depending on how the audience responds, the speaker can retroactively commit to any of the interpretations left open, and so try to retroactively update the common ground. I go on to develop a model of how pied-piping functions that incorporates game-theoretic elements into the more traditional common-ground framework in order to capture the uncertainty of updating. (shrink)

How should we think about the ways search engines can go wrong? Following the publication of Safiya Noble’s Algorithms of Oppression (Noble 2018), a view has emerged that racist, sexist, and other problematic results should be thought of as indicative of algorithmic bias. In this paper, I offer an alternative angle on these results, building on Noble’s suggestion that search engines are complicit in a racial contract (Mills 1990). I argue that racist and sexist results should be thought of as (...) part of the workings of the social system of white ignorance. Along the way, I will argue that we should think about search engines not as sources of testimony, but as information-classification systems, and make a preliminary case for the importance of the social epistemology of technology. (shrink)

We are grateful to the commentators for their close reading of our article 1 and for their challenging and interesting responses to it. We do not have space to respond to all of the objections that they raise, so in this reply, we address only a selection of them. Some commentaries question the usefulness of developing an account of the sort we provide, 2 or of revising the Standard Account (SA) in doing so. 3–5 Our schema is intended to provide (...) a framework for developing a full account of invasiveness that captures existing uses of the term. On the assumption that the term is used somewhat reliably and consistently–and we are inclined to agree with some of our commentators 3 4 that people are not terribly confused or mistaken in how they employ the term–an account that captures these... (shrink)

We investigate the compositional semantics of vague quantified sentences, focusing on sentences such as “All of the students are tall,” where a non-vague quantifier quantifies into a vague predicate. While much work has been done on vagueness in natural language, including the semantics of vague adjectives, little attention has been paid so far to how vagueness interacts with complex sentences. We present an experiment that gathers data on naïve speakers’ interpretation of such sentences after collecting their judgment on the applicability (...) of the vague predicate for each individual in the restrictor. We then compare how three prominent fuzzy logics – Gödel, product, and Łukasiewicz – predict the acceptability of the quantified sentences. Our results indicate that Gödel logic best matches human behavior. We then prove an equivalence between Gödel logic and a probabilistic form of Williamson’s epistemicism for the sentences we have tested, and discuss how our findings inform the broader debate on the semantics of vagueness, particularly between epistemicism and graded-truth approaches. (shrink)