Comparative Discourse Analysis between English and Chinese News Texts： By using functional grammar to analyze the textile dispute report In People’s Daily and New York Times
Bibliographic record
Abstract
Halliday’s systemic-functional grammar has been not only applied in English text analysis, but also in Chinese text analysis. This paper aims at comparatively analyzing English and Chinese in two influential newspapers from the perspective of three metafuctions in systemic-functional grammar. It is also hoped that this paper could be a further test for the applicability of systemic-functional grammar. Key words: systemic-functional grammar, comparative analysis, English and Chinese news texts Resume: La grammaire fonctionnelle systemique de M.K.Halliday sert non seulement a analyser le discours anglais, mais egalement sert d’un bon cadre theorique pour l’analyse du disours chinois. L’article present, commencant par les trois meta-fonctions de Halliday, procede a une analyse comparative des differences de modele entre le discours anglais et le discours chinois dans les medias. L’auteur espere verifier par cette analyse la faisabilite et l’utilite de la grammaire fonctionnelle systemique. Mots-Cles: grammaire fonctionnelle systemique, analyse comparative, textes de nouvelle anglais et chinois
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".