“How the Furby Coming is…”: Interference of First Language and Culture in Thai EFL Learners’ Paragraph Writing
Bibliographic record
Abstract
This study investigated features of first language and cultural interference in Thai EFL learners’ English paragraph writing on popular culture. Drawing from theoretical grounds of interlanguage, language interference, and rhetorical interference, the sample of 30 English paragraphs of Thai EFL undergraduate learners was examined quantitatively and qualitatively. The English writing included 15 paragraphs from the Thai learners with high exposure to English language (TEH) group, and 15 paragraphs from those with the low exposure to English language (TEL) group. Using analysis models of metadiscourse markers and topical progressions, the findings revealed the preference of both groups in the use of interactive and interactional devices as well as SP, PP, and EPP types of topical progressions. The preference highlights the feature of oral-based, inductive, or reader-responsible writing orientation with a possibility of writing development, especially among the TEHs to reach expectation of the target language readers. The findings encourage assessing the Thai EFL learners’ writing as a process and raising frequent awareness of both language and rhetorical interferences when writing English texts. As the introductory stage during COVID-19 remote learning, writing to express learners’ interests could be used as an effective communication strategy for a positive instructor-learner relationship which assists the learners to further engage in the class in a more meaningful way.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".