Status of the Ukrainian Language in the Context of Global Challenges and Military Aggression (Based on the Material of the Modern English-Language Press)
Bibliographic record
Abstract
Russia's military aggression against Ukraine has globally transformed the media landscape. Facing global challenges, the world's media began to continuously publicize the unacceptable violations and catastrophic Russian armed aggression consequences. On February 24, 2022, Russia attacked Ukraine, and these events prompted both Ukrainian and global journalists to refocus on wartime conditions. This work is a compilation of theoretical and methodological approaches that may be useful for the study of the discourse transformations within media discourse. The work is time-limited, but it is during the period in question that the “language issue” roared throughout the pages. The idea of combining the concept of discourse, sociolinguistics, and lexico-semantics to understand the discursive and linguistic event was proposed. These methodologies were grouped around ideas that recognize the relevance of English-language mass media. To study a linguistic event such as the Ukrainian war, the empirical part aimed to illustrate how proceedings such as guilt, linguistic conflict, can be investigated by methods of discourse analysis and other linguistic phenomena. Such a constructivist approach develops the working hypothesis that nomination (as a discursive record) varies according to the work and sociopolitical stakes of the speaker.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".