Neologisms of COVID Era
Bibliographic record
Abstract
This article is an attempt to analyze English neologisms that appeared in the language during the COVID-19 era. The authors examined a series of English-language publications, presented on open-access public domains such as BBC News, The Conversation, Business Mirror, The Economic Times, as well as Glossary on the COVID-19 pandemic, published on the website of the Government of Canada. The chronological scope of the study lies within April 2020 – February 2021. The analyzed glossary included 143 lexical units. The authors conducted content analysis, which helped to reveal five main groups of neologisms: neologisms that came into our speech from the limited use vocabulary; neologisms describing our new reality; neologisms formed by joining two lexical units with or without contamination; neologisms, which are phrases that either existed earlier, but experienced a semantic shift, or phrases that have appeared in the COVID era and are used to denote previously non-existent realities; neologisms formed by phonetic distortion of already existing words. The study showed that the most extensive groups of neologisms were those that have come from the limited use vocabulary, in particular from medical terminology, and neologisms describing a new reality, which include the very name of the virus (COVID or corona). It should be noted that neologisms that have come into general use from medicine require a special interpretation, since they are not always clearly understood by the recipients. Moreover, many neologisms, having arisen in English, have not got an adequate translation or analogue in the Russian language yet, therefore, these words require a further more careful study.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".