A Linguistic Analysis of COVID-19 Neologisms in English: Bangladesh Perspective
Bibliographic record
Abstract
The COVID-19 pandemic has significantly impacted how we use language globally, resulting in an increase in new terms and expressions. Bangladesh, like the rest of the world, has also experienced this linguistic transformation. This study aims to identify and analyze the words, phrases, and acronyms that have gained prominence in English language usage during and after the COVID-19 outbreak in 2020. The study focuses on both global and Bangladesh-specific language usage. Through a systematic literature review and quantitative analysis of a corpus spanning from January to June 2020 in Bangladesh, the study compiled and examined COVID-19-related vocabulary. The objective was to pinpoint the specific terms and expressions that gained prominence in English during this period. Additionally, the study analyzed a selection of the newly emerged words to determine the techniques of word formation. This study's findings enhance our knowledge of language change dynamics and offer valuable insights into the direct relationship between societal upheavals, such as the global pandemic, and language evolution. The research illuminates how language adapts and expands in response to significant events, providing valuable insights into the interplay between language and society.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".