An Empirical Study on Information Prominence Reflected in Sentence Structures of Chinese College EFL Argumentative Writing
Bibliographic record
Abstract
Prominence, as an important dimension of cognitive construal, refers to the capacity to evoke a certain substructure as the focus of attention, which can be materialized in a variety of semantic and grammatical expressions (langacker, 1987). Subject of a sentence (Zhang, 2011) and specific sentence structures (Lin, 2013) can bring a substructure into salience by highlighting it in a specifically grammatical place. Accordingly, the place of subject or the specific sentence structures which are applied to emphasize certain information can reflect a writer’s intention of prominence. Thus, this essay will take prominence of Langacker’s cognitive construal as theoretical basis and has an empirical study on information prominence of 20 argumentative writing papers written by Chinese college EFL learners from Leshan Normal University. By categorizing the sentences of each sample into five types of structures－link verb sentence, active sentence, passive sentence, non-finite verbs as subject and the specific sentence patterns, and referring to the statistics of sentence structure application and the specific writing contents, it is found out that the writers are inclined to highlight subjective or static information, which leads to subjectivity and powerlessness of argumentation.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".