Comprehension of English Loanwords in Japanese by Japanese and English Speakers
Bibliographic record
Abstract
This study addresses our understanding of English loanwords in the modern Japanese language. It aims to investigate the two types of English loanwords and made-in-Japan loanwords among Malaysian English speakers and native Japanese. The proposed study utilized a quantitative approach to determine the understanding of two groups of speakers; 60 Japanese speakers in Japan and 60 English speakers in Malaysia. The data collection of this research was completed using two questionnaires. The two questionnaires consist of 14 sentences with these two types of English loanwords selected from Japanese textbooks and other sources. The findings reflected correct responses to the meaning of English loanwords and made-in-Japan loanwords for Japanese speakers. The English speakers showed correct responses for English loanwords, however, they were discrepancies in responses when it comes to made-in-Japan loanwords. This research breaks ground on the issue of the comprehension of English loanwords and made-in-Japan among Native Japanese speakers and Malaysian English speakers. This study incorporates the theory of language contact by Thomason (2001). The contact occurs where the mutual influence of languages happens leading to code-switching, borrowing, and loanwords formed by the social setting and the contact environment. It also employs the theory of language awareness to support second language learning and develop the learner’s comprehension. The significance of the study emphasizes English language learning benefits and the importance of the learners’ understanding of the differences between English loanwords to utilize them in vocabulary building.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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, 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".