Crosslinguistic influence on pragmatics: the case of apologies by Japanese first-language learners of English
Bibliographic record
Abstract
It is said that Japanese tend to overuse I'm sorry, and a number of studies have confirmed this. Some studies attribute it to Japanese culture; however, does Japanese language have any influence on that? This study, therefore, investigates the uses of the English apologetic phrases, namely, I'm sorry and excuse me by Japanese-L1 learners of English, comparing them with some counterparts in Japanese, sumimasen and gomen (gomennasai). This study also takes the length of residence in English-speaking countries (LOR) into consideration. The data were collected from three different groups: Japanese in Japan whose LOR is less than a year (JJ), Japanese in Montreal whose LOR is over a year (JMtl), and native speakers of English (NSE) in Montreal. Questionnaires and follow-up interviews were administered to answer the research questions.The results showed that JJ group used I'm sorry more often than NSE group. One of the reasons of the overuse of I'm sorry was transfer of Japanese apologetic expressions. However, there were some cases when they said sorry less often than NSE group and JMtl group, and that was probably attributed to their unfamiliarity with the sorry-to-bother-you type of expressions. It was also found that the JJ group sometimes had difficulty using excuse me appropriately, and the reason could be insufficient input of excuse me. Overall, the study showed that Japanese learners' use of these expressions becomes closer to that of the NSE group the longer they stay in an English-speaking country.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".