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Record W7033111074

“¿Por qué me has tirado, Amor, / todo el metal de tu aljaba?”: El episodio de las bodas de Camacho en El amor hace milagros (1784), de Pedro Benito Gómez Labrador

2012· article· es· W7033111074 on OpenAlexaboutno aff

Bibliographic record

VenueDeposito Adademico Digital Universidad De Navarra (University of Navarra) · 2012
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicPhytoplasmas and Hemiptera pathogens
Canadian institutionsnot available
Fundersnot available
KeywordsThe VenerableKingdomPeriod (music)
DOInot available

Abstract

fetched live from OpenAlex

Existen, en el siglo XVIII, numerosas recreaciones dramáticas del Quijote y, de \nentre todas ellas, son bastantes las que toman como punto de partida el episodio de las \nbodas de Camacho.2 Así, tenemos Las bodas de Camacho, comedia nueva jocoseria en \ndos actos (de hacia 1770, conservada en la BNE, Ms. 15.918), de Antonio Valladares de \nSotomayor; Las bodas de Camacho, zarzuela nueva española, de Leandro Ontala y \nMaqueda (1776, BNE, Ms. 14.608); Las bodas de Camacho el rico (1784), de Juan \nMeléndez Valdés, la más famosa de todas; Las bodas de Camacho. Comedia pastoral \nen cinco actos (BNE, Ms. 14.071), además de la pieza que ahora me ocupa: El amor \nhace milagros (también de ese mismo año 1784), de Pedro Benito Gómez Labrador. \nComo ha señalado la crítica, el tema del matrimonio de los jóvenes, y más \nconcretamente el debate sobre si debían ser los padres o los propios hijos quienes \nhabían de decidir a la hora de tomar estado, era una cuestión de enorme actualidad, un \ndebate candente en la sociedad dieciochesca. Es ahí donde hay que buscar, sin duda, la \nrazón de esa preferencia de los dramaturgos por este episodio concreto del Quijote a la \nhora de abordar recreaciones de la obra cervantina.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.122
Threshold uncertainty score0.242

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 studies0.0090.006
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.014
GPT teacher head0.227
Teacher spread0.213 · 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
Published2012
Admission routes1
Has abstractyes

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