The linguistic and cultural aspect of the new vocabulary of the coronavirus pandemic
Bibliographic record
Abstract
The article studies the linguocultural aspect of the emergence of the coronavirus pandemic neologisms. The similarities and differences between the English and Russian vocabulary of the coronavirus pandemic, generated by the specifics of the cultural code of intercultural communication participants, are analyzed. The relevance of the study is ensured by the continuous expansion of the coronavirus pandemic vocabulary, the permanent interest of linguists in the most popular part of the neological discourse of the English and Russian languages, the need for a scientific interpretation of collective experience reflected in the coronavirus pandemic vocabulary. The objective of the article is a linguistic and cultural analysis of the English and Russian coronavirus pandemic vocabulary to identify the peculiarities of the mindset of these neologisms’ creators in the definitions and contextual field of the given examples. The material of the study is the lexical units used in the speech of the inhabitants of the English-speaking (Great Britain, Ireland, the USA, Canada, Australia, India, South Africa) and the Russian-speaking areas recorded on the Internet. The fundamental research method is linguoculturological analysis supplemented by stylistic, semantic-axiological, comparative and componential analyzes. The role of the borrowing factor in compiling the corpus of the coronavirus pandemic vocabulary is also investigated. The regional specifics of lexical units that reflect the consequences of the spread of the disease is revealed. In particular, the article considers the specificity of the reflection of the emergence of new values on the stylistic layer of these neologisms in both Russian and English speeches.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".