Bibliographic record
Abstract
Today, English has become the most widely used language in the world. So understanding and knowing about cultural differences play a more and more important role in intercultural communication. There are various cultural differences between English language and Chinese language, such as greetings, form of address, politeness and social dos and don’ts shape. This paper mainly studies praise and compliments, and analyzes the following aspects: social functions; the sentence patterns of praise language; usage mode and concerned topics, the ways to respond, as well as the social causes of these cultural differences. Therefore, English learners should grasp social cultural knowledge of China and western countries when they studying English. In that case, people could avoid misunderstanding in intercultural communication. Key words：Intercultural communication, cultural difference, praise and compliments, social cause Resume : Aujourd’hui, l’anglais est devenu la langue la plus populaire au monde . Donc , la comprehension et la connaissance des differences culturelles jouent un role de plus en plus important dans la communication interculturelle . De diverses differences culturelles existent entre l’anglais et le chinois , telles que la salutation , formule de s’adresser , de politesse , etc . Ce texte etudie principalement les louanges et les compliments , et analyse ces aspects ci-dessous : fonction sociale , les expressions de louange , la mode d’usage , les themes concernes , les moyens pour repondre ainsi que les causes sociales de ces differences culturelles . Par consequent , les apprenants d’anglais doivent maitriser les connaissances de la culture sociale chinoise et celles des pays occidentaux quand ils apprennent l’anglais . Dans ce cas , on peurrait eviter les malentendus le plus possible dans la communication interculturelle . Mots-cles : communication interculturelle, difference culturelle, louange et compliment, cause sociale
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".