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Record W2324354913 · doi:10.7764/res.2014.34.2

Garganta de piedra: el canto artificial de Alberto Kurapel y la recepción de chilenos exiliados en Montreal durante los setenta

2014· article· es· W2324354913 on OpenAlexaffabout

Bibliographic record

VenueResonancias: Revista de investigación musical · 2014
Typearticle
Languagees
FieldSocial Sciences
TopicPolitical and Social Dynamics in Chile and Latin America
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

La obra musical de Alberto Kurapel sigue siendo un terreno francamente postergado por los investigadores, ya sea por elestatus incipiente en que se encuentra el conocimiento general sobre la música chilena en exilio durante la reciente dictadura, ya por la adscripción más apropiada de este artista al campo de la actuación y la dramaturgia. Lo cierto es que, además de aportar de manera significativa al teatro, se desenvolvió activamente como cantautor en Montreal, el paradero de su destierro, convirtiéndose probablemente en uno de los más prolíficos solistas exiliados en el ámbito discográfico. De sus siete álbumes del período, se toman como objeto de escrutinio los tres primeros: Chili: Amanecerá la siembra (1975), Chili: Guitarra adentro (1977) y A tajo abierto (1978). En particular, se busca examinar la triple relación dada entre ciertas nociones de vocalidad desplegadas en algunas canciones, el desencuentro con un público de chilenos exiliados y la concepción de artificialidad en la performance de exilio. Para ello, se recurre a ensayos del propio Kurapel, así como a los conceptos de marcos de análisis y personae de la teoría de performance musical de Philip Auslander. Asimismo, el artículo basa una buena parte de sus estipulaciones en fuentes orales.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.328

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.006
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0160.001

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 head0.011
GPT teacher head0.296
Teacher spread0.285 · 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 designQualitative
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

Citations2
Published2014
Admission routes2
Has abstractyes

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