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Record W4403374840 · doi:10.1515/9780228023203

Shakespeare and the World of “Slings & Arrows”

2024· book· en· W4403374840 on OpenAlexaboutno aff
Gary Kuchar

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2024
Typebook
Languageen
FieldArts and Humanities
TopicShakespeare, Adaptation, and Literary Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsArt

Abstract

fetched live from OpenAlex

Slings & Arrows , starring Susan Coyne, Paul Gross, Don McKellar, and Mark McKinney as members of the New Burbage Theatre Festival, was heralded by television critics as one of the best shows ever produced and one of the finest depictions of life in classical theatre. Shakespeare scholars, however, have been ambivalent about the series, at times even hostile. In Shakespeare and the World of “Slings & Arrows” Gary Kuchar situates the three-season series in its cultural and intellectual contexts. More than a roman à clef about Canada’s Stratford Festival, he shows, it is a privileged window onto major debates within Shakespeare studies and a drama that raises vital questions about the role of the arts in society. Kuchar reads the television show – ever fluctuating between faith and doubt in the power of drama – as an allegory of Peter Brook’s widely renowned account of modern theatre, The Empty Space, mirroring Brook’s distinction between holy theatre, a quasi-sacred vocation, and deadly theatre, a momentary entertainment. Combining contextualized interpretations of the series with subtle formalist readings, Kuchar explains how Slings & Arrows participates in a broader recuperation of humanist approaches to Shakespeare in contemporary scholarship. The result is a demonstration of how and why Shakespeare continues to provide not just entertainment, but equipment for living.

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)0.003
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: Other · Consensus signal: Other
Teacher disagreement score0.054
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.018
Scholarly communication0.0070.005
Open science0.0000.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.024
GPT teacher head0.207
Teacher spread0.183 · 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
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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