Bibliographic record
Abstract
As one of the most active scholars in the field of Chinese run-on sentence (CRS for short), Wang and Zhao (2016) keenly realize that CRS displays distinctive traits of spatiality, namely, chunkiness, discreteness and reversibility, among which, the last trait and the iconicity of sequence/order (e.g. Haiman, 1984, 1985) seem to depict a diametrically opposite picture. In the present article, there would be an attempt to undertake an investigation of Wang, Zhao et al.’s ‘reversibility’ to see whether or not CRS is an exception to the iconicity of sequence/order. The main arguments are as follows. First, ‘reversibility’ is borne out to be local by some linguistic facts, especially in: (i) duyuju within CRS; and (ii) shuncheng CRS. Second, although the ‘reversibility’ sometimes exhibits a tendency to change the positions of clauses/syntagms in CRS, there is a clear correlation between the clause order of CRS and iconicity. The sequence/order principle in practice emerges as a cognitive mechanism emitting some effects in the clause order of CRS. Third, the Reversibility Condition is required to come into being so as to arrive at a detailed specification of the applicable scope of the ‘reversibility’. And finally, it is more preferable to ameliorate the spatiality of CRS as two traits, that is, chunkiness and discreteness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".