The Discourse Analysis of Social Factors Influencing Interest Contention in Business Dispute Settlement: A Perspective of Discourse Information Theory
Bibliographic record
Abstract
Interest contention which reflects the nature of business dispute settlement is one of the vital issues to explore in the studies of business dispute and it structures the whole process of business dispute settlement from mediation, negotiation to arbitration and litigation. Under the influence of various factors, litigants with differing interest orientations and interest demands could make good use of a number of information resources for the purpose of communicating, defending and fighting for the interests of their own. Contexts are a socially based mental model dynamically constructed by participants about “the for-them-relevant properties” of communicative situation (van Dijk, 2008). The social factors in the context influences the distribution of discourse information resources in the interest contention in business dispute settlement. In view of this, the present study focuses on the discourse analysis of social factors influencing the interest contention in business dispute settlement at the stage of litigation from the perspective of Discourse Information Theory (DIT) (Du, 2007, 2013, 2015). It can be found that any conflicting party’s lawyer could take advantage of both different social identities and social relationships to attack the counterparty’s loopholes or shortcomings and gain more interests for his own party in the interest contention in business dispute settlement.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".