Formulaic Language Use by Learners of English in Interlanguage Communication
Bibliographic record
Abstract
This study investigated the use of formulaic language, in the form of four-word lexical bundles, by Thai learners of English as a second language (ESL) at various levels of English proficiency during interlanguage communication. The investigation focused on two aspects: the frequencies and pragmatic functions of the four-word lexical bundles. A total of 120 Thai ESL learners participated in the study, ranging from basic to intermediate and advanced English proficiency levels. In terms of frequency, a list of the most frequently used four-word lexical bundles by Thai ESL learners at each level was examined. Results showed that learners at higher English levels used less formulaic language in interactions than those at lower levels. Two similar four-word lexical bundles, centered on "I don't know" and "I think," were used by Thai ESL learners at all three English levels. The functional analysis demonstrated that Thai ESL learners at all three English levels used formulaic language to assert group identity as a device for social interaction and as a device for memory limitations to buy more time and process shortcuts. The function of asserting a separate identity was not used by intermediate-level Thai ESL learners. Furthermore, the function of processing shortcuts was used less at the advanced level than at the basic level. The findings of this study indicate that Thai ESL learners use formulaic language differently at different English proficiency levels. Therefore, formulaic language used by ESL learners in spoken English discourse should be further investigated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".