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Record W3195080135 · doi:10.53871/2078-8134.2020.3-03

PATRIOTIC AND HEROIC MOTIVES IN THE POETRY OF ALEXANDR TVARDOVSKY AND KEYSAR AMINPUR

2020· article· en· W3195080135 on OpenAlexaboutno aff
M. Yahyapur

Bibliographic record

VenueKeruen · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSecurity, Politics, and Digital Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsPoetryParallelsPatriotismHomelandLiteratureStyle (visual arts)Quarter (Canadian coin)Literary languageComposition (language)HistoryArtSociologyLawPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Connections and interrelations come up in the global literary process directly and indirectly. The present article is devoted to the literary parallels in the creative work of the famous Russian poet, a participant of the Second World War, chief editor of the literary magazine «Nowy Mir» Alexandr Tvardovsky and the popular Iran poet Keysar Aminpur. War and peace problems – are persistent in any national literature throughout the centuries. These problems have reflected in the life, fate and creative work of the Russian poet. They are also predominant in the poetry of K. Aminpur. Longing for home, dreams about piece in the world during ongoing military actions– are common concepts in the poetry of Tvardovsky and Aminpur, where people striving for peace have been drawn into the conflict. Patriotic and heroic motives in the poetry of the Russian and the Iran poets, ways of reflection in style, language and composition of the poems are different due to literary process particularities in the USSR and Iran in the last quarter of the XX-th century. At the same time, there are common points allowing us to compare poetry of A. Tvardovsky and K. Aminpur. Motives of patriotism and heroics are being analyzed from the prospective of nature. Homeland and smaller motherland landscape concepts are inseparable in the poetry of the both poets.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0040.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.047
GPT teacher head0.299
Teacher spread0.251 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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