A corpus-based multidimensional computational analysis of episodic variation in Chinese relational clauses
Bibliographic record
Abstract
With the purpose of exploring the mechanism of change in Chinese relational clauses, this paper firstly includes transitive verbs, intransitive verbs and adjectives in the study of relational clauses, and carries out a comparative analysis from the perspectives of syntactic form, semantic expression, and distribution of thesis elements, and finds that relational clauses constituted by transitive verbs are indeed the most typical members of Chinese relational clauses.Then, we examine its performance in the type of relativization, main clause syntactic position of core words, vitality pattern, and structural features, and conclude that the argument elements of the relational clauses present a vitality contrast pattern and have a simpler structure with an average of about 4 syllables, while the distribution of the central words of the Chinese relational clauses conforms to the order of the noun-dominant syntactic position.Finally, ERP technology is used to explore the processing advantages of subject-relative clauses and to regulate the vitality and denotation of the verbal thesis elements of the clauses, and it is found that the difference in processing difficulty between subject and object-relative clauses increases when the subject of the clauses is a vital noun and the object is a non-vital noun.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".