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Constitutive Rules and Coherence in Legal Argumentation: The Case of Extensive and Restrictive Interpretation

  • Antonino RotoloEmail author
  • Corrado Roversi
Chapter
Part of the Law and Philosophy Library book series (LAPS, volume 102)

Abstract

The literature in jurisprudence and legal theory has rarely explored the link between argumentation theory and the institutional theory of legal concepts. A remarkable exception that devoted some (non-systematic) effort to this link is, for instance, the work by MacCormick (2005). This paper aims at offering a fresh contribution to this research issue by developing a theory of the extensive and restrictive interpretation of legal provisions. We show that these interpretive techniques correspond to complex revision operations over theories of constitutive rules. Indeed, restrictive and extensive interpretations are typical examples of how courts deal with the open texture of legal language and with penumbral cases by further developing the content of legal rules: courts act in such a way as to expand or restrict the core of determinate meaning of rules taking into account their purposes (Hart, 1994, Chap. 7). Herbert Hart suggested that, in those cases, judges act as surrogate legislatures by filling legal gaps. Indeed, this view is confirmed in this paper and made analytically clear: legal concepts can be holistically and inferentially characterized by arbitrarily large and connected theories of constitutive rules, and so, when we expand or restrict the scope of legal concepts we are doing nothing but changing (revising or contracting) those theories. This analysis also sheds light on the relation between the legal and the ordinary understandings (if any) of a given concept: in the case they both take place, we should compare and aggregate two corresponding theories, the one corresponding to the ordinary reading and the one corresponding to the legal reading.

Keywords

Institutional Fact Legal Rule Constitutive Rule Legal Concept Regulative Rule 
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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Copyright information

© Springer Science+Business Media Dordrecht. 2013

Authors and Affiliations

  1. 1.CIRSFIDUniversity of BolognaBolognaItaly

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