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Is relativism a coherent thesis? The paper argues for a new view of relativism that opposes both classic and contemporary views. On this view, the thesis of relativism is coherent even if the key notions in the standard apparatus of relativism—of alternative conceptual schemes, relative truth, perspectival content—are all incoherent. The view defended here highlights issues about attitude attribution and explanation in formulating the thesis of relativism and it proposes a surprising connection between relativism and nonsense. The paper argues further that the thesis of relativism, so understood, is coherent, by considering different accounts of the semantics of attitude attributions in their application to the attribution of nonsensical thinking. [ABSTRACT FROM AUTHOR]

Background and Objectives Chronological age is invariably used as a categorizing tool for spaces, collections, and programs in public libraries. With age-based conceptions of human experience in library practice, education, and scholarship primarily focused on those under the age 18, little is known how age-based classifications are implemented in public libraries, and with what impacts, for older library patrons. Research Design and Methods Stemming from a larger project that seeks to bring attention to the ways in which public libraries engage with community-dwelling older adults, this paper explores 51 older patrons' perspectives on the numbers and language (e.g. 55+, older adult, seniors, adult) assigned to older adults in library programs and which label best (or least) suits their sense of identity and, in turn, what language encourages or deters their engagement with library programs. Results Findings illustrate that age-based language describing older adult library programs is often at odds with patrons' perceptions of how library programming relevant to them ought to be labeled. Common to all participants was a clear dislike for the term "elderly." While most participants preferred "older adult" to "senior," others voiced no preference, as long as they felt heard and valued. Many participants linked the use of language used to describe library programs to being excluded from and treated differently from other (younger) library patrons. Discussion and Implications The language used to group and describe different library populations directly shapes feelings of belonging (or exclusion) in library programs. Insights from this research contribute to our evolving understandings of the ways in which language connected to age can shape one's sense of identity. Results also serve to cultivate a more sensitive and critical approach to the question of age within library science, and, by extension, the experiences of older adults who frequent the library. [ABSTRACT FROM AUTHOR]

Background Music is an integral part of our lives and is often played in public places like restaurants. People exposed to music that contained alcohol-related lyrics in a bar scenario consumed significantly more alcohol than those exposed to music with less alcohol-related lyrics. Existing methods to quantify alcohol exposure in song lyrics have used manual annotation that is burdensome and time intensive. In this paper, we aim to build a deep learning algorithm (LYDIA) that can automatically detect and identify alcohol exposure and its context in song lyrics. Methods We identified 673 potentially alcohol-related words including brand names, urban slang, and beverage names. We collected all the lyrics from the Billboard's top-100 songs from 1959 to 2020 (N = 6110). We developed an annotation tool to annotate both the alcohol-relation of the word (alcohol, non-alcohol, or unsure) and the context (positive, negative, or neutral) of the word in the song lyrics. Results LYDIA achieved an accuracy of 86.6% in identifying the alcohol-relation of the word, and 72.9% in identifying its context. LYDIA can distinguish with an accuracy of 97.24% between the words that have positive and negative relation to alcohol; and with an accuracy of 98.37% between the positive and negative context. Conclusion LYDIA can automatically identify alcohol exposure and its context in song lyrics, which will allow for the swift analysis of future lyrics and can be used to help raise awareness about the amount of alcohol in music. Highlights Developed a deep learning algorithm (LYDIA) to identify alcohol words in songs. LYDIA achieved an accuracy of 86.6% in identifying alcohol-relation of the words. LYDIA's accuracy in identifying positive, negative, or neutral context was 72.9%. LYDIA can automatically provide evidence of alcohol in millions of songs. This can raise awareness of harms of listening to songs with alcohol words. [ABSTRACT FROM AUTHOR]

The most common approach to understanding the semantics of the concept of pain is third-person thought experiments. By contrast, the most frequent and most relevant uses of the folk concept of pain are from a first-person perspective in conversational settings. In this paper, we use a set of linguistic tools to systematically explore the semantics of what people communicate when reporting pain from a first-person perspective. Our results suggest that only a pluralistic view can do justice to the way we talk about pain from a first-person perspective: The semantic content of the folk concept of pain consists of information about both an unpleasant feeling and a disruptive bodily state. Pain linguistics thus provides new insights into ordinary pain language and poses an interesting challenge to the dominant unitary views of pain. [ABSTRACT FROM AUTHOR]