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Record W3006537243

Tragovi egzistencijalizma u Sábatovu romanu El túnel

2019· article· pl· W3006537243 on OpenAlexaboutno aff
Sara Večeralo

Bibliographic record

VenueHrčak Portal of scientific journals of Croatia (University Computing Centre) · 2019
Typearticle
Languagepl
FieldArts and Humanities
TopicFrench Literature and Critical Theory
Canadian institutionsnot available
Fundersnot available
KeywordsArtHumanities
DOInot available

Abstract

fetched live from OpenAlex

Egzistencijalistička misao nastala polovicom 20. st. u književnosti ostavila je najviše traga oslanjajući se ponajprije na djela francuskih autora Jean-Paula Sartrea i Alberta Camusa. Neke od ključnih tema egzistencijalističke misli poput izoliranosti i alijenacije pojedinca, kao i preokupiranosti temom holokausta te razornih posljedica II. svjetskog rata zastupljene su u djelima kao što su Mučnina Jeana Paula Sartrea, Pad Alberta Camusa, Homo faber švicarskog autora Maxa Frischa te u romanu El túnel Ernesta Sábata. Argentinski pisac Ernesto Sábato u romanu El túnel uprizoruje problem izoliranosti pojedinca u alijeniranom svijetu bez duhovnog usidrenja. Roman nas, kroz vizuru nepouzdanog pripovjedača i za egzistencijalistički roman karakteristično pripovijedanje u prvom licu, uvodi u svijet paranoidnog i opsesivnog slikara Juana Pabla Castela koji najavljuje ubojstvo jedine žene koja je razumjela njegovo slikarstvo – Maríje Iribarne. Slika Majčinstvo koja uprizoruje detalj žene na žalu, sliku u slici, označava bezdanu strukturu, mise en abyme, koja igra ključnu ulogu u interpretaciji čitavog djela. Djelo se pritom može tumačiti i u tzv. „argentinskom ključu” koji naglašava nedostatak duhovnog usidrenja u svijetu preopterećenu materijalnim progresom, odnosno u odbacivanju i istrebljivanju autohtonog elementa Latinske Amerike potisnutog u procesu kolonizacije.

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)0.003
metaresearch head score (Gemma)0.006
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: Empirical · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0080.002
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0530.010

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 head0.019
GPT teacher head0.222
Teacher spread0.203 · 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
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
Published2019
Admission routes1
Has abstractyes

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