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Record W2894742749 · doi:10.7202/1051067ar

Voice in The “Long 20th Century”: From Mechanical to Electrical Aurality1

2018· article· en· W2894742749 on OpenAlexaffvenue
Jean-Marc Larrue

Bibliographic record

VenueRecherches sémiotiques · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicVisual Culture and Art Theory
Canadian institutionsUniversité de Montréal
FundersCentre National de la Recherche ScientifiqueUniversity of CambridgeFordham University
KeywordsPhonographContext (archaeology)Value (mathematics)IdeologyVirtueAestheticsSociologyMedia studiesArtVisual artsHistoryEngineeringComputer scienceElectrical engineeringPolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

The use of microphones in theatre today is so common that it is hard to believe how recent this practice is and, more importantly, that it has provoked such long standing and fierce resistance. The fact is that the theatre, which very quickly integrated the electric lamp (at the end of the 19th century) into its technical arsenal, waited more than a century before resorting to microphones to relay the voices of the actors. Technological imperfections alone are not sufficient to explain this deferment since, between the emergence of the first sound reproduction technologies in the late 1870s (microphone, phonograph, telephone) and the 21st century, four distinct media have enjoyed considerable success on account of these technologies : records, radio, cinema and television. This article argues is that such delay was due to an ideological positioning by which the theatre tried to affirm its ontological superiority over the other media practices by establishing itself as the ultimate refuge of “authenticity” by virtue of the simultaneous – and non-technologically mediated – presence of the actor and the spectator in a single space. In this context, the human voice of theatre took on a highly symbolic value, that of unadulterated authenticity, a value which seemed perverted everywhere else.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.001

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 head0.148
GPT teacher head0.359
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2018
Admission routes2
Has abstractyes

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