The Richness of English Language During Covid-19 Pandemic: Selected Words and Expressions That Can Be Taught to EFL Students at the Colleges of Health Science and Colleges of Nursing in KSA and Kuwait
Bibliographic record
Abstract
The COVID-19 crisis has made the years 2020 and 2021 an unpolitical and spiritual crisis. It has affected virtually everybody in the world and introduced a new normal. Since the beginning of the COVID-19 outbreak, people have been hooked on consuming news media to follow the development of this unprecedented disease. Subsequently, a new language with vocabulary, expressions, and metaphors has appeared in various languages, including English and Arabic. Dictionaries have added new words in English and revised others; they are now fully integrated into our everyday vocabularies. COVID-19 has changed the English language in many ways: it has brought previously obscure medical words to the forefront of everyday speech, made terms related to social isolation more common, and witnessed a shift in meaning in other terms. As linguists, researchers, and teachers gradually return to their classrooms next term (Spring, 2022) we undertook this study to identify 57 English terms, expressions, and metaphors that emerged during the COVID-19 pandemic, either in English-speaking countries or Arabic-speaking countries where English is a first or second language. We deemed the new terminologies necessary for EFL learners in the Gulf Cooperation Countries (GCC) countries. It can serve the purpose of making a list of these words and expressions to be taught to our EFL students at colleges of nursing and health science in Kuwait or Saudi Arabia or any other equivalent colleges in the Arab World. The terms and expressions came from articles, magazines, and English and Arabic dictionaries published during the COVID-19 pandemic.
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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 | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".