A Contrastive Study of Word Sequence of English and Chinese Nominal Groups: A Systemic Functional Approach
Bibliographic record
Abstract
Nominal group is always a heating study object for linguists. There are many scholars who have shed light on this field. However, among the current studies, most of them are just confined into the language of English. The contrastive study of nominal groups in English and Chinese, especially the study of the word sequence of modifiers, is rarely to be found. This paper, adopting a systemic functional approach, mainly conducted under the guidance of Halliday’s interpretation of nominal groups from the experiential point of view, attempts to make a contrastive study of the similarities and differences between Chinese and English nominal groups with respect to the word sequence of their modifiers. On the one hand, this paper is a tentative study of word sequence of the modifiers both in Chinese nominal groups and English nominal groups, aiming to make a general description of them; on the other hand, through the contrast, we are trying to enable the readers to have a better understanding of the mechanism of the modifiers in nominal groups in both languages.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".