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Shakespearean Retreats: Spectrality, Survival, and Autoimmunity in Kristian Levring’s The King Is Alive

  • Maurizio Calbi
Part of the Reproducing Shakespeare: New Studies in Adaptation and Appropriation book series (RESH)

Abstract

In the “Exordium” to Specters of Marx, Jacques Derrida elaborates on the enigma encapsulated in the expression “to learn to live” (“apprendre à vivre”), an expression that describes “a strange commitment.” It is simultaneously impossible—”[t]o live, by definition, is not something one learns. Not from oneself, it is not learned from life, taught by life”—and necessary—this “wisdom … is ethics itself” (xvii-xvii).1 To learn to live, Derrida suggests, can only come from the other; more specifically, it can only come “from the other at the edge of life.” It is, he adds, “a heterodidactics between life and death” (xviii, emphasis added). Because of the irreducible trace of alterity inscribed within it, to learn to live turns out to be a spectral, indefinite, and interminable process that exceeds any living present. It can only happen “between life and death. Neither in life nor in death alone” (xviii). In a sense, therefore, to learn to live is, first of all, to acknowledge, and bear witness to, the originary temporal structure of life as “sur-vie” (Learning 26), a living-on, a survival that does not wait for death and is not merely added on to a life that preexists it. It is to learn that life implicates ghosts; that it can only “maintain itself with some ghost, can only talk with or about some ghost [s’entretenir de quelque fantôme]” Put differently, learning to live corresponds to the uncanny aporetic process of learning to live with ghosts, “in the upkeep, the conversation, the company, or the companionship, in the commerce without commerce of ghosts” (xviii).

Keywords

White Hair Namibian Desert Fellow Traveler Collective Remediation White Flake 
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

© Maurizio Calbi 2013

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  • Maurizio Calbi

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