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Toulmin’s Model of Argumentation

  • Frans H. van Eemeren
  • Bart Garssen
  • Erik C. W. Krabbe
  • A. Francisca Snoeck Henkemans
  • Bart Verheij
  • Jean H. M. Wagemans
Living reference work entry

Abstract

In this chapter, Toulmin’s contribution to argumentation theory is discussed. Toulmin presents in his model of argumentation a novel approach to analyzing the way in which claims can be justified in response to challenges. The model replaces the old concepts of “premise” and “conclusion” with the new concepts of “claim,” “data,” “warrant,” “modal qualifier,” “rebuttal,” and “backing.” Because of the impact Toulmin’s ideas about logic and everyday reasoning have had, he can be regarded as one of the founding fathers of modern argumentation theory.

In Sect. 4.2, the study The Uses of Argument, in which Toulmin expounded his views and explained his model, is introduced. Sect. 4.3 concentrates on the geometrical model of validity that is, according to Toulmin, at the heart of the misunderstandings about formal logic he wants to terminate. The distinction he makes in this endeavor between analytic and substantial arguments is treated in Sect. 4.4. In Sect. 4.5, the difference between field-invariant and field-dependent aspects of argumentative discourse is explained, which is vital to the alternative to the formal approach to analytic arguments offered by Toulmin. In Sect. 4.6 the forms arguments take and their validity are discussed, which leads to the presentation of Toulmin’s new model of argumentation in Sect. 4.7. Sect. 4.8 focuses on appropriations of the Toulmin model by argumentation theorists from different backgrounds. Sect. 4.9 discusses various applications of the model. In Sect. 4.10, the chapter is concluded with a critical appreciation of Toulmin’s contribution to argumentation theory.

Keywords

Argument Scheme Valid Argument Logical Type Major Premise Formal Validity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Frans H. van Eemeren
    • 1
  • Bart Garssen
    • 1
  • Erik C. W. Krabbe
    • 2
  • A. Francisca Snoeck Henkemans
    • 1
  • Bart Verheij
    • 3
  • Jean H. M. Wagemans
    • 1
  1. 1.University of AmsterdamAmsterdamThe Netherlands
  2. 2.University of GroningenGroningenThe Netherlands
  3. 3.Faculty of Mathematics and Natural SciencesUniversity of GroningenGroningenThe Netherlands

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