A Systemic Functional Analysis on Discourse Marker—“Honest Phrases”
Bibliographic record
Abstract
Discourse marker is one of the most important research topics in recent years. Many researchers from home and abroad have explored it from various perspectives, including the perspective of discourse coherence; the perspective of syntax-pragmatics; the perspective of cognitive pragmatics and the perspective of metapragmatics. At the same time researchers at home mainly make specific analysis on certain discourse markers in terms of their pragmatic function. Besides, Systemic Functional Linguistics is a branch of Functional Linguistics, being further divided into systemic grammar and functional grammar. Systemic grammar regards language as a system network or meaning potential to explain; while functional grammar intends to prove language is a social interaction manner, emphasizing the function of language. However, at present a few researchers have combined the two fields to study together. Based on the fact and the above theoretical foundation, the paper introduces discourse marker to a broader category and studies it in a new perspective. The paper selects “Honest Phrases” in two different communication interaction—daily conversation and police interrogation to analyze discourse marker-“Honest Phrases” using three metafunctions in Systemic Functional Linguistics, aiming to explore its multifunctional mechanism in discourse from perspectives of ideational metafunction, interpersonal metafunction and textual metafunction so as to make up the shortage of the former study perspectives of “Honest Phrases” and to help people understand it deeply.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.001 | 0.003 |
| 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".