Cross-Cultural Comparisons of English Request Speech Acts in Native Speakers of English and Chinese
Bibliographic record
Abstract
This paper aims at comparing the uses of the English request speech acts in native speakers of English and Chinese. An oral discourse completion task (ODCT) was used to collect data and the chi-square analysis method was applied to examine the data. From the results, the comparisons of request strategies and internal modifications between Chinese and English native speakers showed no significant differences; both groups frequently used indirect strategies. However, with regard to the use of alerts and external modifications, significant differences were found between these two groups. Further results also indicated the effects of social status and familiarity on both groups. To interlocutor in higher status, both groups showed significantly different usages of internal and external modifications. As to interlocutors in equal status, they performed different request strategies, alerts and external modifications. In addition, significant differences were found in the use of alerts to interlocutors in lower social status. To familiar interlocutors, both groups showed different usages in alerts and external modifications. To unfamiliar interlocutors, significant differences were also found in the use of alerts and external modifications. At last, Chinese native speakers with high and low proficiency levels showed significantly different usages in alerts. Key words : English request speech act; Oral discourse completion task; Chi-square analysis; English native speaker; Chinese native speaker
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".