NEOLOGISMS THAT APPEARED IN THE VOCABULARY OF KAZAKHSTANIS DURING THE CORONAVIRUS PANDEMIC
Bibliographic record
Abstract
All languages on the planet are responsible for various social situations and phenomena that occur in society. The Kazakh language is also constantly developing, replenishing, and changing. Every day, new words appear in his vocabulary, become obsolete, some of them are out of Use, and some of them change in meaning and are updated again. The recent pandemic and the current epidemiological situation in the country have also affected the language. The covid-19 coronavirus infection pandemic and restrictive measures have brought significant changes to the public life of the whole world, including Kazakhstan, as well as gave an impetus to the formation of new names through the internal resources of the language, the productivity of some wordforming tools, the rapid flow of language processes. In fact, Corona has added new terms to the vocabulary of all languages of the world. Along with the pandemic, many words such as social immunity, masks, vaccinations, distance protection, covid, etc.have entered the vocabulary of Kazakhstanis. The article analyzes neologisms related to the coronavirus pandemic that have appeared in the Kazakh language or whose meaning has been updated. Lexical innovations of the coronavirus era, such as coronaviruses, coronaviruses, and coronaviruses, appeared in the language in 2020 and in a short period of time entered the common language and took a place among the words that form a combination with a high frequency of use.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".