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Record W7020334164

Las orquestas de los coliseos madrileños Príncipe y Cruz durante la segunda mitad del siglo XVIII

2016· article· en· W7020334164 on OpenAlexaboutno aff

Bibliographic record

VenueGredos (University of Salamanca) · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicMedia, Journalism, and Communication History
Canadian institutionsnot available
Fundersnot available
KeywordsPublic workContext (archaeology)Period (music)Quarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

There were no stable orchestras in Madrid’s public theatres during the first half of the Eighteenth Century, and a variable number of instrumentalists were used on each occasion. This paper discusses the development of the two town-owned theatres in the second half of the century, when the orchestra was considered an important element and a new way of hiring musicians developed, based on sources kept at the Archivo de Villa de Madrid, Sección de Secretaría./nRené Andioc and Mireille Coulon in their Cartelera teatral madrileña del siglo XVIII: (1708-1808) refer to the companies that worked in both theatres in the second half of the 18th Century, those of de Josef de Parra, María Hidalgo, José Martínez Gálvez, Juan Ángel, Águeda de la Calle, María Ladvenant, Nicolás de la Calle, Juan Ponce, Manuel Martínez, Eusebio Ribera, Joaquín Palomino, Luis Navarro and Francisco Ramo, although they do not study the orchestral musicians. The first study on this issue is that by José Máximo Leza «Las orquestas de ópera en Madrid entre los siglos XVIII-XIX», although it does not discuss the theatres of La Cruz and El Príncipe in the second half of the century.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0030.000
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.017
GPT teacher head0.193
Teacher spread0.176 · 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
Published2016
Admission routes1
Has abstractyes

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