A Functional Study of Lexical Conversion within Modern Chinese Nominal Group
Bibliographic record
Abstract
The phenomenon of lexical conversion within modern Chinese nominal group is often presented in ancient Chinese grammar. For many years, there have been earnest discussions in China about how we can better study the Chinese nominal group from alternative dimensions, e.g. cognition, pragmatics, multi-category words, word-class shift as well as functional perspective, but few pay attention to the lexical conversion from perspective of Systemic Functional Linguistics (SFL). As the SFL itself is “a problem-oriented theory” (Huang, 2006), to apply this theory to explain some certain language phenomena merits serious consideration. This paper is based on the Cardiff Grammar, an important model of SFL and the purpose is to explore the semantic and syntactic function in lexical conversion within modern Chinese nominal group. Through the contrastive study in light of the Cardiff Grammar, the Chinese nominal group can be functionally used as a Main Verb, a Main Verb Extension (MEx) and a prepositional group (pgp).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".