Coronaspeak as Key to Coronaculture: Studying New Cultural Practices Through Neologisms
Bibliographic record
Abstract
The research explores neologisms that have entered everyday English discourse during the coronavirus pandemic and formed so-called Coronaspeak. The analysis reveals that three approaches to neologisms are applicable to lexemes of Coronaspeak: the stylistic theory that is relevant to the words that used to be scientific terms but have been adopted by non-specialists, the etymological approach that regards as neologisms those new coinages that have developed a new meaning, and the denotational approach where neologisms are the lexemes created to nominate new concepts. Drawing on the assumption that language units verbalise cultural phenomena, the further study of Coronaspeak suggests that the modern English-speaking societies undergo a number of cultural changes: medicalisation of public discourse that originates from the government policy to engage the public in the struggle against COVID-19 as well as from using the pandemic as an argument in ideological and political conflicts; conceptualisation of the pandemic as a milestone, a turning point in history; introduction of new categories for social groups based on such criteria as health, profession, or attitude to the pandemic and socially responsible behaviour (e.g., clinically vulnerable people, key workers, covadults); development of new or modification of old cultural practices that embrace lifestyle (coronacocooing, WFH, drivecation), appearance (corona hair, coronabesity), patterns of online and offline communication (homeference, video party, coronadating, Wuhan shake); reconceptualisation of pre-pandemic concepts (home), and, finally, emergence of new types of interpersonal relations (coronarelationship, corona boyfriend).
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".