How did COVID-19 impact the use of Japanese complex words with <i>masuku</i> ‘mask’ in 2020?
Bibliographic record
Abstract
Abstract This paper examines how the situation caused by COVID-19 impacted the use of a well-entrenched word in Japanese: masuku ‘mask’. An inspection of data gathered from an online newspaper shows a sharp increase in token and type frequency in the use of complex words with masuku ‘mask’ in 2020 (mid-pandemic) compared to 2019 (pre-pandemic), implying the recurrence and variegation of mask-related topics in the media. Focusing on the varied types of complex words containing masuku ‘mask’, the paper offers a construction morphology account of how they distribute within a network of words. The most dominant means to expand the network was compounding, creating not only hyponyms of masuku ‘mask’ (i.e., using masuku as the head of the compound, as in ago-masuku ‘chin mask’) but also hyponyms of other well-entrenched words (i.e., using masuku as the non-head, as in masuku-gimu ‘mask obligation’). Beyond compounding, a playful use of language in blends led to the creation of a new path, albeit a small one. The paper argues the development of the word network involved both mundane and exceptional creativity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".