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Record W4377224620 · doi:10.1515/lingvan-2021-0143

How did COVID-19 impact the use of Japanese complex words with <i>masuku</i> ‘mask’ in 2020?

2023· article· en· W4377224620 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLinguistics Vanguard · 2023
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsYork University
Fundersnot available
KeywordsNewspaperCompoundingCoronavirus disease 2019 (COVID-19)SentenceComputer scienceAdvertisingHistoryArtificial intelligenceSociologyMedia studiesBusiness

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.084
GPT teacher head0.353
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it