Bibliographic record
Abstract
Presupposition refers to what is assumed by the speaker when uttering a specific sentence, usually realized by the useof particular lexical items and/or linguistic constructions which are known as presupposition triggers, is an evitablerequirement for interpretation of the utterance. Based on the theme-rheme theory and thematic progression patternssuggested by Hu Zhuanglin and Zhu Yongsheng, the author adopts qualitative method to make an analysis of thetextual function of presupposition which is realized by serving as themes in clauses, helping to construct differentthematic progression patterns and transmitting information in business letter discourse. Presupposition helps toconstruct four main thematic progression patterns in business letter discourse, which can be conclude as T1→T2 (thesame theme), R1→R2 (the same rheme), R1→T2 (the rheme or part of the rheme in the previous clause becomes thetheme of the next clause) and T1+R1=T2 (both the theme and rheme of the preceding clause are encapsulated into anoun phrase functioning as the theme of the frequent clause). The author chose 30 business letters selected from theinternet and several textbooks as the object of research, and they cover almost every stage of foreign trade.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".