“We Wear Face Shield and Mask”: COVID-19 Related Words and Phrases Used by Thai Learners of English
Bibliographic record
Abstract
Since 2020, measures against the COVID-19 pandemic have been implemented worldwide, and these are reflected in language. The objectives of this study are to explore the use of COVID-19 - related words and terms in Thai learners of English, document their usage, and investigate their varieties and errors, and suggest pedagogical implications for using authentic online materials in teaching English. The data were the written language that were collected from January 2021 to July 2021 from online Facebook groups administered by students of an open university in Thailand. A qualitative descriptive method of analysis was used. Words and terms related to the pandemic were thematically categorized and analyzed considering loanwords and borrowing. Patterns of use were analyzed and compared with corpora. The findings emerged from the analysis. There are a number of COVID-19 related loanwords from English used by Thai learners of English and the conventionalization of these loanwords, varieties and errors are observed. Most of the loanwords were used in code-mixing, and this is likely the source of errors when Thai learners use these words in their English. The findings have some pedagogical implications. The paper recommended that teachers identify and correct students’ errors immediately. Students should sometimes also be given some explanation about the errors in order to prevent potential overgeneralization of word use. Furthermore, the paper recommended that further research be carried out on the new terms that have been transferred to Thai language as loanwords, loan-translations and loan-blends.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".