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Record W4400881580 · doi:10.33137/rr.v47i2.43685

Valdemoros, Ana, and David Amelang, project dirs. Crossdressed Characters in Early Modern European Theatre

2024· article· en· W4400881580 on OpenAlexvenueno aff
Alexandra E. Lagrand

Bibliographic record

VenueRenaissance and Reformation · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Influence and Diplomacy
Canadian institutionsnot available
Fundersnot available
KeywordsArt

Abstract

fetched live from OpenAlex

de Madrid, Crossdressed Characters aims to curate a collection of transgender and cross-gender characters within the early modern European dramatic canon.The data that underpins this collection is derived from the larger Rolecall database, a project that itself seeks to chart gendered language used by characters in early modern European theatre.Crossdressed Characters takes this notion of gender in speech and performance even further, however, by focusing on characters that perform onstage in costume not typically associated with their perceived gender.Employing a transnational approach to early modern theatre, this database provides a useful resource for scholars and practitioners beginning to explore and understand gender performance in early modern drama.Furthermore, this resource comes at a time when illuminating these cross-gender performances is more important than ever.Because Crossdressed Characters is hosted and powered by Rolecall, it is, perhaps, useful to first briefly discuss this larger resource.The overall aim of Rolecall is to chart visually the speech of characters in early modern European theatre within the sixteenth and seventeenth centuries.More specifically, Rolecall allows users to visualize the proportions of lines given to female and male characters in order to examine aspects of protagonism.While the Rolecall site houses both the Rolecall and Crossdressed Characters databases, it is clear that Crossdressed Characters is an extension of the main Rolecall project, which is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.Because Crossdressed Characters is hosted by Rolecall, it appears that it, too, falls under this license.Roles are selected for inclusion in Crossdressed Characters based on inclusion in Rolecall, which thus far features sixteenth-and seventeenthcentury plays from England, France, Germany, Italy, the Netherlands, Portugal, and Spain.Tracking gendered speech in early modern plays is inevitably a

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)0.007
metaresearch head score (Gemma)0.022
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.092
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0020.001
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0920.026

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 head0.311
Teacher spread0.287 · 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
Published2024
Admission routes1
Has abstractyes

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