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Record W7067882544

Mykolo Sluckio kūrybos lenkiškieji kontekstai

2017· other· lt· W7067882544 on OpenAlexaff

Bibliographic record

VenueThe Repository of University in Bialystok (University in Bialystok) · 2017
Typeother
Languagelt
FieldBiochemistry, Genetics and Molecular Biology
TopicYeasts and Rust Fungi Studies
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPeriod (music)Social lifeSubject (documents)
DOInot available

Abstract

fetched live from OpenAlex

Žymus lietuvių prozininkas Mykolas Sluckis (1928–2013) atėjo į lietuvių literatūros istoriją kaip talentingas lyrinės prozos meistras. Pirmieji jo romanai išsiskiria emocinių impulsų srautu, metaforine stilistika ir asociatyvine kompozicija. Rašytojas siekė per modernų vidinį monologą parodyti žmogaus psichiką. Pagrindiniu Sluckio herojumi išliko žmogus su palaužta sąmone, nepatenkintas savimi ir aplinka, praradęs gyvenimo tikslą. Apie Sluckio kūrybą rašė Vytautas Kubilius, Algis Kalėda, Petras Bražėnas, Algimantas Bučys, Vytautas Martinkus, Leonidas Terakopianas išleido monografiją. Literatūrologai atkreipė dėmesį į šiuolaikinės psichologinės analizės legalizavimą lietuvių literatūroje, socialistinės visuomenės ydų kritiką, asmens tapatybės ir svetimumo problemas šiuolaikinėje visuomenėje. Bet niekas neįsigilino į lenkiškąją temą Sluckio kūryboje. Romane Geri namai tai nauja vaikų namų auklėtinė Jadvyga, romane Kelionė į kalnus ir atgal vieno iš pagrindinių herojų pusbrolis sveikinasi lenkiškai, kioskininkė Vanda iš romano Uostas mano – neramus palieka herojui lenkišką žurnalą „Kobieta i žycie“. Šiame straipsnyje bandoma analizuoti Mykolo Sluckio kūrybos lenkiškąją temą.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.033
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0330.005

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 head0.009
GPT teacher head0.201
Teacher spread0.192 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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