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Record W4240196643 · doi:10.1353/mdr.2012.0026

Subversion, Refusal, and Contingency: The Transgression of Liberal-Humanist Subjectivity and Characterization in Sarah Kane’s Cleansed , Crave , and 4.48 Psychosis

2012· article· en· W4240196643 on OpenAlexvenueno aff
Cristina Delgado-García

Bibliographic record

VenueModern Drama · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanismSubjectivitySociologyAestheticsPsychoanalysisNormativeSubversionQueerPhilosophyIdeologyPerformativityEpistemologyGender studiesPoliticsTheologyPsychologyLawPolitical science

Abstract

fetched live from OpenAlex

Drawing on Judith Butler’s writings on performativity, this article investigates the critique of the liberal-humanist understanding of selfhood offered in Sarah Kane’s last three plays. It argues that Cleansed subversively deploys conventional dramatic character to disfigure the subject as it is understood by liberal humanism. The motif of the wound activates this subversive characterization, with the supposed self-sameness or coherence of selfhood being dismembered on the page. Subjectivity emerges in this playtext as discursively produced, defined by the body, and processual. Conversely, Crave and 4.48 Psychosis reject the terms through which such liberal-humanist subjectivity is represented. Through uncountable and opaque characters, subjectivity is formulated as queer, foreclosing the equation of body morphology with the sense of the self and dismantling fixed and unified notions of identity. Finally, the article interrogates to what extent the staging of the plays inevitably reproduces stable characters and normative subjectivities. Desire and recognition are considered here as key to maintaining the plays’ critique of liberal humanism.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.009
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0080.084
Scholarly communication0.0090.006
Open science0.0010.008
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.231
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2012
Admission routes1
Has abstractyes

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