2020
DOI: 10.1111/mila.12297
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A pluralistic theory of wordhood

Abstract: What are words and how should we individuate them? There are two main answers on the philosophical market. For some, words are bundles of structural–functional features defining a unique performance profile. For others, words are noneternal continuants individuated by their causal‐historical ancestry. These conceptions offer competing views of the nature of words, and it seems natural to assume that at most one of them can capture the essence of wordhood. This paper makes a case for pluralism about wordhood: T… Show more

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Cited by 6 publications
(3 citation statements)
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“…The data get more complex when we consider a word's relation to its meaning. Gasparri (2020) gives a few examples that illustrate further puzzles:…”
Section: The Datamentioning
confidence: 99%
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“…The data get more complex when we consider a word's relation to its meaning. Gasparri (2020) gives a few examples that illustrate further puzzles:…”
Section: The Datamentioning
confidence: 99%
“…Can they be associated to a single polysemous word, or should they be understood as senses of numerically distinct words? (Gasparri 2020) In some cases, it looks like the same word like 'skyline' can change meaning, and in other cases it looks like different meanings of 'fluke' make the same-looking word a different word. In the next section I look at whether the type-token model is equipped to handle data like these, concluding that the model falls short of offering a satisfying explanation for the data above.…”
Section: The Datamentioning
confidence: 99%
“…Though the details of his account are widely disputed, it is often thought that something in this vicinity must be right.For discussion, seeBurge (1975), Skyrms (1996,Marmor (1996),Jackman (1998),Marmor (2009),Millikan (1998),Gilbert (1989), and Gilbert (2008).16 For Lewis, this is truthfulness and trust in L. SeeLewis (1969),Lewis (1975).17 See, e.g.,Capellen (1999),Gasparri (2016),Gasparri (2020),Irmak (2019),Leckie and Williams (2019),Miller (2019),Mallory (2020), and Neft (2019).…”
mentioning
confidence: 99%