Tracking the language of COVID-19 for communication: at a glance… / Profesor Madya Dr. Norwati Hj Roslim ... [et al.]
Bibliographic record
Abstract
Word of the Year 2020: Collins Dictionary has declared lockdown as the word of the year due to its sharp rise in usage during the COVID-19 pandemic (https://www.collinsdictionary.com/). Merriam-Webster’s Word of the Year for 2020 is pandemic due to its extremely high numbers of looked up in online dictionary (https://www.merriam- webster.com/). The Oxford English Dictionary (OED), however, has been unable to name its traditional Word of the Year for 2020, instead exploring how far and how quickly the language of COVID- 19 has developed in its report titled, "Words of an Unprecedented Year" (https://edition.cnn.com/). Corpus Analysis of the Language of COVID-19: The Coronavirus Corpus (Mark Davis, 2020) highlights what people are actually saying in online newspapers and magazines in 20 different English- speaking countries. This includes words and phrases such as social distancing, flatten the curve and pandemic (https://www.english-corpora.org/). A comparison between regions shows, although the word front liner is used worldwide, it is particularly frequent in South East Asia, especially the Philippines and Malaysia. Self-quarantine is more common in the US than in Canada, Great Britain, Ireland, Australia and New Zealand, where self-isolate and self- isolation are preferred. Words occurring near frontline are “frontline nurse/ medic/caregiver”, “frontline healthcare/health-care workers”, “frontline warrior/hero”, “courageous/heroic frontline workers” and “essential frontline worker” (https://public.oed.com/).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.078 | 0.085 |
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".