Avoidance in Processing English Non-restrictive Relative Clauses in Thai EFL Learners’ Interlanguage
Bibliographic record
Abstract
This paper examined the acquisition of English non-restrictive relative clauses (NRCs) by L1 Thai learners, focusing on avoidance strategies applied in the learners’ interlanguage. The theoretical frameworks of the Noun Phrase Accessibility Hierarchy (NPAH) and the Perceptual Difficulty Hypothesis (PDH) were the main hypotheses predicting avoidance behavior employed by the learners. The research participants were 80 Thai EFL high school students of high and low proficiency levels. The data were elicited through a sentence combination task and a Thai-English translation task. The findings, overall, suggested that Thai EFL learners, by and large, avoided using the more marked NRC types in the NPAH. The learners’ avoidance was evident in the tasks from the shifting of more marked types to less marked ones. The findings also showed that the more advanced learners tended to employ more avoidance behavior with regard to the NPAH, which may be attributed to their greater exposure to the L2 rules and more knowledge of the differences between the L1 and L2 structures; this resulted in the use of alternative ways to complete the given tasks more easily. The center-embedded NRCs, more precisely, were found to be problematic for learners, which supports the prediction of the PDH that clauses embedded after the subject positions may interrupt the processing of a complex structure of RCs. The learners, therefore, avoided center embedding and changed it to right embedding with little awareness of the ungrammaticality of the NRC sentences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".