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Record W2118511669 · doi:10.3138/md.0663

Aesthetic Radicalism: Langston Hughes’s Lost Translation of Federico García Lorca’s <i>Bodas de sangre / Blood Wedding</i>

2014· article· en· W2118511669 on OpenAlexvenueno aff
Michelle Woods, Sarah Wyman

Bibliographic record

VenueModern Drama · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicSpanish Literature and Culture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical radicalismOppressionPoliticsVernacularStyle (visual arts)Reading (process)LiteratureArtArt historyPhilosophyLawPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT: Langston Hughes’s long-lost translation of Federico García Lorca’s play Bodas de sangre / Blood Wedding (1933) – Fate at the Wedding (1938) – demonstrates a synchronicity between two 1930s aesthetic radicals who shared a transnational perspective. Through his act of translation, Hughes not only perpetuates and amplifies Lorca’s concern with social issues but he uses his own version of Lorca’s total theatre, with its graphic and sound effects, to deploy a unique blend of avant-garde European and fringe modernist styles of the Latin American and Caribbean worlds he knew intimately. Hughes’s act of translation opens a politicized space in the play that reveals his own aesthetic project, wrought in terms of colour, vernacular discourse, and a hybrid notion of American musicality and culture. He thus melds his subversive message with Lorca’s own and highlights the latent radicalism of the Spanish playwright’s aesthetics and politics. At a time when he was intensely politicized and often didactic in his writings on race and class oppression, Hughes chooses to infuse the experimental style of Lorca’s play with political potentiality, reading it as an internationalist study of the mechanics of oppression, without reducing it to a single political or racial reading.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.215
Teacher spread0.200 · 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 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
Published2014
Admission routes1
Has abstractyes

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