Bibliographic record
Abstract
The use of the Dislocation Focus Construction (DFC) (also known as “Right Dislocation”) in colloquial Chinese (including Cantonese and Mandarin) gives rise to various non-canonical word orders. In DFCs, the sentence particle (SP) occurs in a sentence-medial position. The pre- and post-SP materials are demonstrated to be syntactically connected, based on four diagnostic tests, namely (i) the zinghai ‘only’ test, (ii) the doudai (“ wh -the-hell”) test, (iii) polarity item licensing, and (iv) Principle C violations. The findings offer new insights into the syntax of the Chinese left periphery and constraints on focus movement. First, the observations entail that Chinese CPs are head-initial, and an XP is obligatorily moved around the SP to a position higher than the CP. Second, the XP-raising in the DFC is argued to be driven by focus because of the focus interpretation induced. It is discovered that the focus movement is subject to the Spine Constraint, which turns out to be remarkably similar to the properties of the Nuclear Stress Rule (e.g., selection of focus set and metrical invisibility). It is argued that the DFC is the syntactic realization of the rule.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".