On the Rearrangement of Syntactic Constitutes for Information Structure
Bibliographic record
Abstract
Generally speaking, information structure deals with the sequencing of given and new information in information transmission and it is usually characterized as the interface of syntax, semantics and pragmatics. Most researchers adopt a dichotomy of information structure-given and new information, assigning different labels to syntactic constituents based on their own background theory. But these labels only indicate the functions of different syntactic constituents, without involving their effect on syntactic construction. That is, the researchers mainly adopt a direction-inverted approach to the research: from syntactic construction to information structure. Based on the above issues, this paper, attempts to make an analysis of how information structure is syntactically realized, i.e., the motivations for the syntactic realization of information examines pragmatic motivations for the syntactic realization of information structure. In this part, we still adopt the traditional terms for the division of information structure, such as “topic-focus”, etc. And we suggest that it is different conversational situations that first constrain the potential sequencing of information units, and it is this sequencing of information units that further affects our choice of syntactic constructions.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".