Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".