Bibliographic record
Abstract
In this paper, the conceptual metaphor in cognitive linguistics is used as a theoretical framework for the study of conceptual metaphors in business negotiation in English and Chinese discourse. Firstly, conceptual metaphors in English and Chinese business negotiation discourse are collected and classified. Secondly, the similarities and differences of metaphors in English and Chinese discourse are compared. In this paper, by means of comparison and illustration, the method of qualitative research is adopted. It collected a large number of business cases and business negotiation dialogues, summed up several kinds of metaphors in English and Chinese business negotiation which are in a higher frequency. Through the comparative analysis of these categories, the same points and different points are summed up. Finally, it’s the effects on business English Major Students through the analysis of the results, and how should more reasonable teaching methods be taken. However, due to cultural differences, the usages of conceptual metaphors in different cultures are also different. In a word, conceptual metaphor plays an important role in business negotiation. Based on the conclusion of the comparative study, it is helpful to the study and research of business English leaders. At present, since the research is not comprehensive, we are looking forward to the theory and method of more developed and applied to the business negotiation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".