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Record W2092723142 · doi:10.14198/amesn.2014.19.10

Motivos de son de Nicolás Guillén desde perspectivas teóricas sobre la representación del Otro en la novela testimonio latinoamericana y en la etnografía posmoderna

2014· article· es· W2092723142 on OpenAlexaff
Miguel Arnedo‐Gómez

Bibliographic record

VenueAmérica sin nombre · 2014
Typearticle
Languagees
FieldArts and Humanities
TopicCultural and Social Studies in Latin America
Canadian institutionsNovelis (Canada)
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

Este artículo propone considerar la primera colección de poemas negros Motivos de son (1930) del escritor cubano Nicolás Guillén como un tipo de literatura etnográfica que supera algunos de los problemas formales y metodológicos en otras representaciones de la oralidad del subalterno en la literatura latinoamericana. El análisis incluye comparaciones entre Motivos de son y la fórmula tradicional para el testimonio latinoamericano, sobre todo con referencia a Cimarrón del autor cubano Miguel Barnet y a Me llamo Rigoberta Menchú y así me nació la conciencia de la autora venezolana Elizabeth Burgos. Se demuestra que Motivos de son es una obra innovadora para su época y en bastantes sentidos se aproxima a formas de representación del otro sugeridas por teóricos de la llamada etnografía posmoderna, como James Clifford. En el análisis también se moviliza el concepto de la polifonía literaria del pensador ruso Mijaíl Bajtín para dar cuenta del carácter polifónico de la colección y explicar su singularidad e importancia dentro de la obra poética de Guillén.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.012
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.015
GPT teacher head0.274
Teacher spread0.259 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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