A Comparative Study of the Complaint Strategies among Iranian EFL and ESL Students – The Study of the Effect of Length of Residence and the Amount of Contact
Bibliographic record
Abstract
The purpose of this study was to investigate the pragmatic transfer Iranian EFL and ESL learners of English showed when complaining in English. The study aimed to find out if there exists any relationship between the amount of contact with English and pragmatic competence of both EFL and ESL groups as well as the relationship between the duration of stay in English-speaking countries and the pragmatic competence of complaining in the ESL group, living and studying abroad for some years. For this purpose, the data were gathered from four groups: twenty Iranian native speakers of Farsi and twenty English native speakers of English, as the baseline groups, as well as twenty EFL and twenty ESL learners, as the interlanguage data groups. The data were elicited through a personal information form and a DCT in which they were asked to answer six imaginative situations including complaint-required situations. The DCT data were examined to see to what extent the time spent with English and the duration of stay in target countries and the pragmatic competence of EFL and ESL respondents were related. The findings revealed no significant relationship between the amount of contact, the time spent abroad and the pragmatic competence of Iranian EFL and ESL learners.
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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 | 0.002 | 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 | 0.001 | 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".