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Two phenomena behind the terminology of face.

  • Published In: Journal of Politeness Research: Language, Behavior, Culture, 2023, v. 19, n. 2. P. 323 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Lacroix, René 3 of 3

Abstract

In politeness research and other areas, scholars use a range of metaphorical expressions involving the term face, as in "lose face", "threaten face" and "save face", drawing upon Goffman's paper "On face-work" (Goffman, Erving. 1967. Interaction ritual: Essays on face-to-face behavior. New York: Pantheon Books), often through Brown and Levinson's influential theory of politeness (Brown, Penelope & Stephen C. Levinson. 1987. Politeness: Some universals in language usage. Cambridge: Cambridge University Press). The present paper argues that the interactional processes referred to by such expressions are of at least two kinds, here labeled "Observed-Behavior (OB) face-processes" and "Expressed-Attitude (EA) face-processes". X's OB face loss occurs when others negatively evaluate X on the basis of her behavior; X's EA face loss occurs when others convey to X that they do not have the same values as her ("positive face") or act in a way that impedes her freedom ("negative face"). Ten differences between OB and EA face-processes are set out. These differences are not acknowledged in the literature, which, as shown in this paper, leads to much ambiguity and confusion. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Politeness Research: Language, Behavior, Culture. 2023/07, Vol. 19, Issue 2, p323
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2023
  • ISSN:1612-5681
  • DOI:10.1515/pr-2022-0044
  • Accession Number:165037550
  • Copyright Statement:Copyright of Journal of Politeness Research: Language, Behavior, Culture is the property of De Gruyter and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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