Cultural Factors Influencing Interest Contention of China’s Business Dispute Settlement: A Discourse Information Perspective
Bibliographic record
Abstract
Interest contention is the embodiment of the essential issue in the process of business dispute resolution. In order to realize the interest competition in the settlement of business disputes, the litigants with different interest positioning and interest demands can use many information resources to express, cling to and fight for their interests under the influence of various factors. The present study attempts to make a discourse analysis of the cultural factors that influence the conflict of interests in China’s business dispute court hearings from the perspective of Discourse Information Theory. This research adopts the discourse information analysis method with the aid of “Legal Information Processing System Corpus (CLIPS)”. The analysis is mainly carried out from the perspective of cultural value, thinking mode, business culture and legal culture embodied in the interest competition in the settlement of business disputes. Under the influence of cultural factors, discourse information has different characteristics in the interests of business dispute resolution. The cultural factors and discourse information characteristics that influence interest competition in China’s business dispute settlement found in this study will complement and enrich the cultural research on interest competition in business dispute resolution, and promote the integration of different disciplines of business, law and linguistics, which has certain theoretical and practical significance.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".