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Record W2015108264 · doi:10.3138/md.52.1.38

Things, Voices, Events: Harold Pinter's <i>Mountain Language</i> as Testamental Text

2009· article· en· W2015108264 on OpenAlexvenueno aff
Stephen Watt

Bibliographic record

VenueModern Drama · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicSamuel Beckett and Modernism
Canadian institutionsnot available
Fundersnot available
KeywordsConversationLiteratureHistoryLinguisticsPhilosophyArt

Abstract

fetched live from OpenAlex

Critical conversation about the work of Samuel Beckett has been immeasurably enriched by the meditations of such thinkers as Alain Badiou and Pascale Casanova, both of whom – for very different reasons – have elevated Beckett's Worstward Ho (1983) to the status of “testamental” or “recapitulatory” text: that is, a text that, in Badiou's phrasing, serves to “take stock of the whole of Samuel Beckett's intellectual enterprise.” This essay on Harold Pinter endeavours to make a similar claim for his short 1988 play Mountain Language. In particular, and through the unique theoretical interventions of Badiou, Bill Brown, and Cary Wolfe, “Things, Voices, Events: Harold Pinter's Mountain Language as Testamental Text” delineates the myriad strategies of repressive forces in their ongoing project of domination. These prominently include the priority of the voice, the activation of material “things” as psychical weapons, and frequent recourse to a discourse of species to elide boundaries between the human and the animal, all of which provide a means to re-read the canon of Pinter's work.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.015
Scholarly communication0.0080.006
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.022
GPT teacher head0.255
Teacher spread0.233 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

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