The Discourse Analysis of E-Business Instant Communication from the Perspective of Politeness Principle
Bibliographic record
Abstract
With the popularity and frequency of e-business activities, e-business instant communication plays an increasingly important role in e-business, and the appropriate and reasonable use of business language usually directly influences the economic interests. Therefore, the present study takes politeness principle as the theoretical framework and the participants’ chat records of e-business instant communication as the research data, and adopts the methodologies of discourse analysis and interview to explore the language use in e-business activities from the perspective of politeness principle. And the present study finds that servicers and customers use different linguistic resources from the perspective of politeness principle out of different interest pursuit. More specifically, servicers strictly observe the six maxims having no violations in e-business instant communication, while customers go against Tact Maxim, Generosity Maxim, Approbation Maxim and Modesty Maxim and usually comply with Agreement Maxim and Sympathy Maxim, nevertheless, they violate Agreement Maxim and Sympathy Maxim on special occasion in e-business instant communication. What’s more, if customers can strictly observe Agreement Maxim and Sympathy Maxim, and servicers can study how to avoid and deal with customers’ violation to Agreement Maxim and Sympathy Maxim successfully, enterprises, servicers and customers will benefit.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".