A Corpus-Based Study on Mood Combination Preference in Two-Clause Composite Sentences in Modern Chinese
Bibliographic record
Abstract
Every clause is associated with a specific expressive intention and bears a specific mood: declarative, interrogative, imperative or exclamative. Different moods are combined with the juxtaposition of clauses. A compound sentence has a homogeneous mood combination between its constituent clauses, while the mood in a complex sentence is usually counted on its main clause with the mood in its subordinate clause(s) drowned. Clauses in a Chinese sentence, however, are independent in terms of mood; that is to say, the mood of the whole sentence is the combination of moods of each clause. Tendency for mood combination of two-clause composite sentences in modern Chinese is demonstrated as follows: 1) Homogeneous mood combinations greatly exceed heterogeneous ones; the “declarative + declarative” type outnumbers other types; and there are more combinations with a declarative mood than those without; 2) The more convincing the meaning of a particle indicates, the more frequently the corresponding mood appears in the first part of the combinations; and the mood realized by a modal adverb appears in the second part if another mood is not realized by a modal adverb; 3) A conjunction highly restricts the mood combination; and the frequency of mood combination in coordinate and causal clauses is approximately equal, much higher than that in adversative clauses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".