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Record W4281754538 · doi:10.13035/h.2022.10.01.48

Creación de los clásicos para el siglo XXI: conversación con GRUMELOT

2022· article· es· W4281754538 on OpenAlexaff
Erin Alice Cowling, Glenda Y. Nieto-Cuebas, Esther Fernández

Bibliographic record

VenueHipogrifo Revista de literatura y cultural del Siglo de Oro · 2022
Typearticle
Languagees
FieldPsychology
TopicHistorical and Modern Theater Studies
Canadian institutionsMacEwan University
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

En esta entrevista con los co-directores y fundadores de Escuela Nave 73 y Grumelot Compañía Teatral, hablamos de la situación actual de la pandemia y cómo han implementado técnicas mixtas en sus puestas en escena, tanto antes como después del Coronavirus. Como creadores de un teatro innovador, quienes ya incorporaban los medios digitales en el escenario, poseían los conocimientos y recursos necesarios para digitalizar sus obras. Sin embargo, la utilización de medios digitales no siempre resulta conveniente. Además, sus adaptaciones minimalistas tienen un efecto maximalista sobre la audiencia y siempre están muy conscientes de sus decisiones y de las posibles consequencias que pueden tener sobre su performance, su audiencia y la recepción de su obra.

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)0.005
metaresearch head score (Gemma)0.008
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 score0.025
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.009
Scholarly communication0.0090.006
Open science0.0010.004
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0100.002

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 head0.027
GPT teacher head0.316
Teacher spread0.290 · 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
Published2022
Admission routes1
Has abstractyes

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