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Record W2916120984 · doi:10.3138/md.0950

How to Remediate; or, Gertrude Stein and Virgil Thomson’s <i>Four Saints in Three Acts</i>

2019· article· en· W2916120984 on OpenAlexvenueno aff
Elicia Clements

Bibliographic record

VenueModern Drama · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicVisual Culture and Art Theory
Canadian institutionsnot available
Fundersnot available
KeywordsMusicalOperaArtNegotiationVisual artsLiteratureArt historySociology

Abstract

fetched live from OpenAlex

In this article, I explore the art form of opera as a process of remediation, a particular type of intermediality in which one medium is represented in another. Focusing on the visual domain, Jay David Bolter and Richard Grusin argue that remediation dominates digital media today. The Stein–Thomson collaboration is a fascinating precursor not only because of the writer’s and composer’s shared attentiveness to experimentation within and across media but also because of their mutual interest in complicating presumptions about aurality. By investigating Four Saints in Three Acts as remediation, we learn how the media of language (visual, oral, and aural) and sound (musical and performed live) participate in intermedial negotiations – and how, when media are combined for and in performance, they act on each other.

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 score0.932
Threshold uncertainty score0.655

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)0.0010.000

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 head0.034
GPT teacher head0.235
Teacher spread0.200 · 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
Published2019
Admission routes1
Has abstractyes

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