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Record W4239368585 · doi:10.1353/mdr.2007.0032

How to Do Nothing with Words, or Waiting for Godot as Performativity

2007· article· en· W4239368585 on OpenAlexvenueno aff
Richard Begam

Bibliographic record

VenueModern Drama · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicSamuel Beckett and Modernism
Canadian institutionsnot available
Fundersnot available
KeywordsPerformative utteranceRealismPerformativityNothingPhilosophyAestheticsRepresentation (politics)Reading (process)Object (grammar)ArtEpistemologyLiteratureLinguistics

Abstract

fetched live from OpenAlex

This essay analyzes how Waiting for Godot exposes the structural logic of both rhetorical and dramatic performativity. Drawing on the language-philosophy of J.L. Austin and Ludwig Wittgenstein, Richard Begam considers what happens to "performative" locutions – statements that actually make things happen, such as "I now pronounce you man and wife" – when they are theatrically represented. Austin claims that such locutions when uttered on stage are rendered intransitive – i.e., they lose their performative force – and are therefore relegated to the Kantian realm of the purely aesthetic. Yet Beckett's play spends two acts demonstrating that the primary function of language is not "constative" – not meant to give us a picture or representation of reality. Rather, Beckett's conception of language – drawn from his reading of Mauthner – is essentially performative. But if words, phrases, and sentences all function performatively, if all descriptive uses of language are, in fact, instrumental uses, then the larger effect is to return illocutionary or transitive force to the theatre. As a result, Beckett's play breaks through the wall not only of Ibsenian realism but also of Kantian aestheticism, reclaiming for the dramatic event the kind of "there-ness" that Alain Robbe-Grillet discovered in the first performances of Godot.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.073
GPT teacher head0.275
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2007
Admission routes1
Has abstractyes

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