Bibliographic record
Abstract
Since the term, Chinese Run-on Sentence (CRS for short), comes up firstly in Lu’s (1979, p.27) fundamental book Issues on Chinese Grammatical Analyses, many have cared deeply about it from multi-faceted aspects. However, early discussions proceed at a descriptive level without explicit elaboration of intricate facts within CRS, and some even stagnated, resulting from the complexity of CRS’s unique features, subject reference and logical relations as well as early scholars’ inclination to study CRS from Indo-European syntactic perspectives. Until Shen (2012), based on a very thought-provoking discussion of Chao’s (1968) minor sentences, reemphasizes the primacy of CRS, much headway of the recent past has been made. Given that, in the present article, there would be an attempt to depict the great accomplishments of the past. In our view, the researches dealing with CRS can fall into four parts: working definition, sentence categories, prosodic nature and structural properties, the details of which can be encapsulated as follows.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".