Improving EFL Writing Through Study of Semantic Concepts in Formulaic Language
Bibliographic record
Abstract
Within Asian EFL contexts such as South Korea, large class sizes, poor sources of input and an overreliance on the Grammar-Translation Method may negatively impact semantic and pragmatic development of writing content. Since formulaic language is imbued with syntactic, semantic and pragmatic linguistic features, it represents an ideal means to evaluate the influence of Asian EFL contexts on writing. Thus, formulaic language within academic texts from Korean university students was compared to that found in essays written by American university students. Results revealed that Korean EFL learners overused transitions to define the organization of academic texts at the expense of developing content. Moreover, they used repetition, general lists, and all-purpose formulaic language to “pad” content, neglecting to consider semantic or pragmatic purposes of the text. In contrast to their Korean EFL counterparts, American university students used formulaic language for a variety of pragmatic purposes such as involving the reader, putting examples into a larger perspective, adding connotation, and addressing the perspective of the reader. It appears that EFL contexts such as South Korea require pedagogical and curricular reforms which foster the development of writing composition for semantic and pragmatic purposes.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".