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Record W2041316436 · doi:10.3138/md.53.2.232

Reappropriating the Pace-Egging Tradition: Ewan MacColl's <i>St. George and the Dragon</i>

2010· article· en· W2041316436 on OpenAlexvenueno aff
Claire Warden

Bibliographic record

VenueModern Drama · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDramaGeorge (robot)PaceNarrativePlot (graphics)IndigenousKey (lock)LiteratureHistoryAestheticsArtSociologyArt historyGeographyComputer science

Abstract

fetched live from OpenAlex

Best known as a folk singer, Ewan MacColl wrote upwards of twenty plays; some of his most effective works are his adaptation plays, contemporary dramatic works using canonical playtexts as catalysts. In 1964–65 MacColl wrote a modernized version of the Lancastrian Pace-Egging Play, St. George and the Dragon. Taking this indigenous theatrical form, often associated with the industrial rather than rural landscape, MacColl wrote a play for and about the people, in a specific tradition of urban folk drama. By changing and modifying the traditional play, including new characters, and altering plot, the playwright was able to create a piece that specifically explores some of the key aspects of twentieth-century society. Furthermore, MacColl weaves in a highly politicized narrative thread that culminates in a call for revolutionary change. This unpublished play is a prime example of MacColl's “vandalism” of established forms. This article examines the play, presenting it as a key example of MacColl's experimental aesthetic, a way of appropriating techniques and narrative methods for a new audience, a new society, and a new political objective.

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 score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0100.011
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.203
Teacher spread0.189 · 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
Published2010
Admission routes1
Has abstractyes

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