A cognitive-functional approach to topic constructions in Beijing Mandarin
Bibliographic record
Abstract
Mandarin Chinese has long been accepted as a topic-prominent language.However, a consensus has never been reached on how to define or characte rize the notion of topic.Given the pragmatic orientation of the notion and the heterogeneity of topic-comment constructions, a cognitive-functional approach has been adopted in this research to analyze a spoken corpus of Beijing Mandarin.On the basis of what has been found about structurally identiflred topics in the corpus, I have argued that topic constructions are best taken as specific instantiations of the schema of Conceptual Reference Point for the ensuing comments.' Spoken data are more indicative of online processing than are written texts.Taking advantage of this, I have examined how pauses and pause particles are interrelated with topic marking and topic verbalization.A comparison between marked grammatical subjects and unmarked ones has revealed that the former are more heavily coded.Following the iconicity principle or the accessibility theory as proposed by Givn ( 1983), Ariel (1988, 1990), etc., topics convey more information than subjects.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".