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Record W4399546967 · doi:10.17721/2520-6397.2024.1.04

Corpus-Based Analysis of the Concept France

2024· article· en· W4399546967 on OpenAlexaboutno aff
Maryna Kostiuk

Bibliographic record

VenueLinguistic and Conceptual Views of the World · 2024
Typearticle
Languageen
FieldArts and Humanities
Topiclinguistics and terminology studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceNatural language processingLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

The article focuses on a corpus-based analysis of the concept FRANCE. The analysis of concepts through the lens of corpus linguistics allows us to determine the general perception of a particular reality. Given the current political context and the development of diplomatic relationships, the concept FRANCE becomes significant and requires analysis. As the material for our study, we chose the corpus of Ukrainian language GRAK. General Regionally Annotated Corpus of Ukrainian (GRAC) is a large representative collection of texts in Ukrainian accompanied by a program that enables customization of subcorpora, searching words, grammatical forms and their combinations as well as post-processing of the query results. For this analysis, journalistic and literary texts dated from 1991 to 2022 were selected. The lexeme “France”, representing the concept FRANCE, appeared 189,178 times in GRAK between 1991 and 2022 with the majority of occurrences found in journalistic texts. Besides, other lexical representatives of the concept FRANCE were analyzed, such as “French”, “Paris”, “France”. The article pays particular attention to the contexts in which the concept FRANCE is realized. Ten main thematic groups related to the concept FRANCE were identified and analyzed: FRANCE – PRESTIGE; FRANCE – REFUGE; FRANCE – HISTORY; FRANCE – LAW; FRANCE – POLITICS; FRANCE – LANGUAGE; FRANCE – ECONOMY; FRANCE – SPORT; FRANCE – FOOD; FRANCE – STYLE. Key adjectives and verbs that verbalize the concept FRANCE in the corpus were found. These words often evoke images of well-known politicians and the names of European countries. Moreover, crucial collocates were determined. Thirty collocates representing the lexeme France were identified: Germany, Macron (Emmanuel), Francois (Hollande), President, Italy, Britain, Ministry of Foreign Affairs, Spain, Merkel, Sarkozy, Championship, Leaders, Paris, Ambassador, Team, Elections, PSG, Finance, Embassy, Canada, Government, Lady, Great, Match, Ukraine, Protests, Authority, Visit. These collocates predominantly align with themes of politics, international relations and sports. The extensive usage of the concept FRANCE in Ukrainian corpus indicates a strengthening of political relations between Ukraine and France.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.987
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.267
Teacher spread0.226 · 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 teacher head, 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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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