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Record W4409948107 · doi:10.1515/jcde-2025-2007

Digital Spoken Word Theatre in the UK: Navigating the Theatre Screen with Rose Condo’s <i>The Geography of Me</i>

2025· article· en· W4409948107 on OpenAlexaboutno aff
Shefali Banerji

Bibliographic record

VenueJournal of Contemporary Drama in English · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRose (mathematics)ArtSpoken wordWord (group theory)Visual artsHistoryLiteratureLinguisticsPhilosophyPoetry

Abstract

fetched live from OpenAlex

Abstract Spoken word theatre appeared on the scene of British poetry performance in the 1990 s. The art form privileges the (hyper-)visibility of the poet-performer where practitioners present their work in their customary style in a long form live show. In the course of the COVID-19 pandemic, several previously on-site shows moved to the domain of the digital. In this article, I explore four types of such virtual adaptations by women practitioners with emphasis on performance strategies and the politics of visibility. I then engage in an in-depth analysis of The Geography of Me by UK-based Canadian poet Rose Condo to study the salient features and discontents of digital spoken word theatre and Condo’s use of pre-recorded material. I also examine how this strategy negotiates the expectation of visibility of a gendered body in spoken word theatre. I argue that the use of pre-recorded material helps control the unpredictability of online performance conditions and mitigates the risks associated with performing traumatic narratives.

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)0.005
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: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.579

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.010
Scholarly communication0.0070.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.011
GPT teacher head0.223
Teacher spread0.211 · 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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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