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Record W4382199508 · doi:10.3138/md-66-2-1279

“A Terrible Art of Sharp-Shooting at the Audience”: Teaching the Shock of Modernist Drama via the Play of Ideas

2023· article· en· W4382199508 on OpenAlexvenueno aff
Christopher Grobe

Bibliographic record

VenueModern Drama · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDramaFeelingAestheticsIdeal (ethics)Shock (circulatory)PoliticsComicsPower (physics)SociologySet (abstract data type)Visual artsLiteratureArtEpistemologyLawPolitical sciencePhilosophyComputer science

Abstract

fetched live from OpenAlex

One of the hardest things to convey to present-day readers of modernist drama is the power it had to shock turn-of-the-century audiences. Defining shock as a “structure of feeling” (Raymond Williams) that was pursued in especially teachable ways by the modernist “play of ideas,” this essay shares a set of pedagogical strategies for helping students to feel this shock while also reflecting on the political implications of an art committed to shock. Among these teaching strategies are two attempts to set this “terrible art” (G. B. Shaw) in new, revealing contexts: (1) comparing the modernist theater to non-theatrical practices that attach ideas to structures of feeling besides shock, and (2) pairing modernist plays that “shock” their audience in pursuit of a feminist politics with contemporary plays that “destroy the audience” (Young Jean Lee) in pursuit of an anti-racist politics. The result is not to lionize shock as a universal ideal, but to explore it as a tactic—useful in some settings, harmful in others; always distributing its benefits and its costs unevenly.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.012
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.029
GPT teacher head0.250
Teacher spread0.221 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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