Identifying Taxis and Logico-Semantic Relations in Chinese EFL Writing Samples
Bibliographic record
Abstract
In the context of EFL writing in China, there is a prevalent tendency among students to heavily rely on the provided model essays from teachers, leading to a phenomenon known as sameness. This overreliance often results in confusion when it comes to expressing ideas effectively in sentences. To address this issue, this paper adopts an analytical framework based on the theory of Systemic Functional Linguistics (SFL). SFL is a linguistic theory that emphasizes language’s social context and meaning (Halliday, 1985; 1994). Notably, SFL prioritizes meaning over form and examines the organization of texts as well as individual sentences. The theory has been widely applied in discourse analysis and language teaching. This paper offers a brief literature review on key SFL terms, such as language rank, clause, clause complex, taxis, and logico-semantic relation. Additionally, the paper presents examples of identifying taxis and logico-semantic relations using two types of EFL writing samples from SFL perspective. The analysis reveals that the first text, a comparison and contrast essay, predominantly employs the relation of extension, whereas the second text, an argumentative piece, prominently exhibits the logico-semantic relation of enhancement. The paper provides insight into the use of SFL as an analytical tool for analyzing variable text types. Students learning from these analytical tools may have more grammatical awareness in identifying clause boundaries and logico-semantic relations across different writing genres.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 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".