A Corpus-Based Study of the Use of Temporal Markers in English Writing of Thai EFL Writers
Bibliographic record
Abstract
The current study aimed to investigate the tendency to use temporal markers (TMs) in English writing by three different levels of Thai writers. Data used in this study were based on the Corpus of Thai Writers of English. The corpus size of approximately 8,800,000 words consisted of essay writing produced by intermediate and advanced student writers and professional writers from two Thai English newspapers. Fraser’s (2005) taxonomy of temporal markers was used as an analytical framework. Microsoft Excel was used to calculate the frequency of occurrences, while a Statistical Package for the Social Sciences (SPSS) was the primary research tool for data analysis. A one-way ANOVA reveals a statistical difference in the use of TMs among three groups of writers (p =.000). The intermediate and professional writers were found to prefer using temporal markers at a comparable ratio. However, the intermediate group used temporal markers in a more diverse attitude than the advanced ones. Six temporal markers (as soon as, immediately, meantime, meanwhile, originally, and subsequently) were hardly utilized in writing by all groups of writers. However, no significant differences were found regarding preferences on the position of the TM in the sentences. The findings improve teaching and learning of cohesive devices, especially temporal markers, in EFL writing classes.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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".