Word Order in Mandarin Chinese and Grammatical Relations
Bibliographic record
Abstract
It has been argued by LaPolla (1993LaPolla ( , 1995) ) and Van Valin and LaPolla (1997) --among others -that word order in Mandarin Chinese (henceforth MC) is (almost) exclusively determined by informational/communicative considerations.Though it cannot be denied that word order does encode informative/communicative considerations such as identifiability, foregrounding, and focalization, it is argued here that word order also encodes grammatical relations and that word order in MC can be nicely accounted for if stated in terms of subject, direct and indirect object.To put it briefly, in both communicatively unmarked and marked basic/ordinary sentences, the subject must occur in immediate pre-verbal position (except for unidentifiable subjects in basic sentences), and the indirect object necessarily appears in immediate post-verbal position.If subjects and (morphologically unmarked) indirect objects occupy any other position, the sentence is ungrammatical.As for the direct object, it must appear in immediate post-verbal position in unmarked ordinary monotransitive sentences and immediately after the indirect object in unmarked ordinary ditransitive sentences.Note that the thematic role of the subject, direct object, or indirect object does not affect word order in any way.* Following Lambrecht (1994) and Mel'čuk (2001), the hash sign # stands for 'communicatively unacceptable, though grammatically well formed'.† Lambrecht (1994: 143) briefly mentions that Chinese encodes unidentifiability of an NP by word order inversions.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".