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Record W1996221249 · doi:10.1017/s0266464x07000310

Locating Never Land: <i>Peter Pan</i> and Parlour Games

2007· article· en· W1996221249 on OpenAlexaboutno aff
Anne Varty

Bibliographic record

VenueNew Theatre Quarterly · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicFolklore, Mythology, and Literature Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyPerformance artStyle (visual arts)CentennialInterpretation (philosophy)CurrencyArt historyHistoryArtSociologyMedia studiesVisual artsLawPoliticsPolitical sciencePhilosophyArchaeology

Abstract

fetched live from OpenAlex

John Doyle's centennial adaptation and direction of Peter Pan for the Oxford Playhouse in December 2004 provides the starting point for a fresh investigation of the play. As Clive Barker has placed the playing of games as central to the training of actors, so Doyle's setting for this production, which never departs from the Darling nursery, places games at the heart of his interpretation of this play about learning to act an adult part. The adult roles in Doyle's production all display a small element of Peter Pan, as their acting style requires them to step in and out of role, to demonstrate the playfulness of their actions, and so to resist growing up. These observations invite a backward glance at the ideological significance of play for late nineteenth-century thinkers about childhood. An exploration of playground and parlour games that formed a common currency of childhood experience during the late Victorian era is set against the documentary evidence of games which Barrie played with the Llewellyn Davies boys, and these in turn are shown to be not just manifest within Peter Pan itself, but also to afford the play a structural and ideological cohesion. Anne Varty is a Senior Lecturer in the English Department at Royal Holloway, University of London, and her monograph Children and Theatre in Victorian Britain will be published later this year.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.910
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.227
Teacher spread0.215 · 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 teacher head, 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

Citations1
Published2007
Admission routes1
Has abstractyes

Explore more

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