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Record W3133377612 · doi:10.33896/porj.2020.3.2

Funkcja ekspresywna języka rosyjskiego w wielojęzycznym środowisku młodzieży szkół polskich na Litwie i Ukrainie.

2020· article· en· W3133377612 on OpenAlexaff
Kinga Geben, Mária Zielińska

Bibliographic record

VenuePoradnik Językowy · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLanguage and Culture
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsLinguisticsPsychologySociologyPhilosophy

Abstract

fetched live from OpenAlex

This paper attempts to present the sociolinguistic situation of the multilingual youth from Lithuania and Ukraine, with a particular focus on the function of the Russian language examined from the angle of emotional language behaviours and the Russian-Polish confrontation related to translation of a cultural text. The material basis is the results of a questionnaire survey carried out by the authors among students of schools with Polish as the language of instruction in Vilnius, Lviv, and Horodok in 2018. The comparative analysis covered the linguistic self-assessment of the representatives of two student environments and models of verbal expression of positive and negative emotional states. The analysis of the cultural and linguistic competence was performed on the example of a translation of Russian marked reduplicative expressions. As a result of the analysis, it can be stated that Russian is perceived and used by the surveyed multilingual youth as the most expressive language code.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.041
GPT teacher head0.304
Teacher spread0.262 · 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 designObservational
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

Citations3
Published2020
Admission routes1
Has abstractyes

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