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Record W6986167637

Pablo Neruda y la guerra civil española: vivencias, relaciones, exilio y esperanza

2015· article· es· W6986167637 on OpenAlexaboutno aff

Bibliographic record

VenueRUIdeRA - Institutional University Repository (University of Castilla-La Mancha) · 2015
Typearticle
Languagees
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsSpanish Civil WarDictatorshipMedieval historyPoetry
DOInot available

Abstract

fetched live from OpenAlex

Uno de los trabajos más importantes y desconocidos del poeta Pablo Neruda no tuvo nada que ver con la literatura. El premio Nobel tuvo una relación estrecha con España, ya que vivió en este país durante diferentes momentos de su vida. Además, conoció y departió con una gran cantidad de literatos, políticos, artistas y personalidades españolas destacadas de la primera mitad del siglo XX, en especial con Federico García Lorca. Una vez iniciada la guerra civil española (1936-1939), se alineó desde el primer momento con el bando republicano. Dedicó varios libros de poesía a este conflicto bélico e, incluso, organizó un congreso de literatura en Madrid. Una vez concluida la guerra, prestó una gran atención a los exiliados republicanos que se vieron obligados a cruzar la frontera con Francia a inicios de 1939 a causa de la represión franquista. Después de conocer la durísima situación en la que vivían, Neruda convenció al presidente de su nación para que permitiera el asilo en su país. Gracias a la labor del poeta, se botó finalmente el Winnipeg, un barco que llevó a cerca de dos mil quinientos refugiados republicanos españoles a Chile.

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)0.002
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: none
Teacher disagreement score0.131
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.006
Scholarly communication0.0050.002
Open science0.0000.004
Research integrity0.0010.002
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 head0.020
GPT teacher head0.213
Teacher spread0.194 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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