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The Paradox of Situated Knowledge: Toward an Existential Embedment Theory of Perceptual Truth.

  • Published In: Journal for the Theory of Social Behaviour, 2024, v. 54, n. 4. P. 632 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Zhao, Shanyang 3 of 3

Abstract

Truth is a thorny issue for the sociology of knowledge. In emphasizing social influence on knowledge production, sociologists tend to disavow the objectivity of truth and slide down the slope of the consensus theory and the nihilism of post‐truth. In fighting science denial, on the other hand, sociologists often find themselves aligning with the correspondence theory and the naïve notion of naked truth. The aim of this article is to advance a position that recognizes social influence on knowledge attainment without obviating the objectivity in truth. The key to this argument lies in the concept of existential embedment that unifies objectivity and subjectivity in human struggles for survival and prosperity, from which truth originates and to which truth contributes. Existential embedment also anchors knowledge by providing the foundation for truth validation. Depending on the modalities of knowledge attainment, truth is divided into four types with varying degrees of veridical certitude, each having its own criterion for validity assessment. Knowledge that passes the validation test is accepted as truth within the horizon of the given realm of existential embedment, which may change as the existential activity of the knower changes. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal for the Theory of Social Behaviour. 2024/12, Vol. 54, Issue 4, p632
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2024
  • ISSN:0021-8308
  • DOI:10.1111/jtsb.12436
  • Accession Number:181663269
  • Copyright Statement:Copyright of Journal for the Theory of Social Behaviour is the property of Wiley-Blackwell 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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