JOURNAL ARTICLE

Dualism and Anti-Dualism in the Anthropocene: Process Sociology and Human/Nature Relations in the Great Evolution.

  • Published In: Historical Social Research, 2023, v. 48, n. 1. P. 190 1 of 3

  • Database: Sociology Source Ultimate 2 of 3

  • Authored By: Saramago, André 3 of 3

Abstract

The contemporary ecological crisis challenges the human sciences to develop analytical frameworks that do not treat “nature” as simply the background of human activity. In this context, there are numerous calls for an abandonment of the “anthropocentrism” that colours most approaches to the human sciences, along with the dualism these establish between “nature” and “humanity,” and their substitution with more “ecocentric” perspectives. This article is a contribution to this ongoing debate. With reference to a process sociological understanding of human/nature relations, it proposes a theoretical avenue to overcome anthropocentric dualism via the process sociological conception of “levels of integration” in the “great evolution” of the planet, while making the case for the need to preserve a theoretically relevant awareness of the evolutionarily emergent distinguishing characteristics of the human species. Without an understanding of these emergent characteristics, and the developmental paths these have opened in the history of the species and the planet, neither the origins nor the adequacy of the answers to the ecological crisis can be properly understood. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Historical Social Research. 2023/01, Vol. 48, Issue 1, p190
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2023
  • ISSN:0172-6404
  • DOI:10.12759/hsr.48.2023.09
  • Accession Number:163297649
  • Copyright Statement:Copyright of Historical Social Research is the property of GESIS - Leibniz-Institute for the Social Sciences 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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