Complaint Behaviors among Native Speakers of Canadian English, Iranian EFL Learners, and Native Speakers of Persian (Contrastive Pragmatic Study)
Bibliographic record
Abstract
The purpose of the present article is to make a contrastive cross-cultural pragmatic analysis between Canadian native speakers, Iranian EFL learners, and Iranian native speakers of Persian in regard to the speech act of complaint. To do so, in the first stage, a Nelson Proficiency Test was administered among 20 Canadian university students majoring in different fields,20 among Iranian EFL learners, and 20 among Persian speakers, respectively. Based on the results of this test, those who scored highly on the test were selected as the main participants of the study. In the second stage, an open-ended questionnaire in the form of a Discourse Completion Task (DCT) comprising of 30 authentic complaint situations was administered among the three groups. It should be noted that the DCTs were translated into Persian for the third group of participants. The data was analyzed using a non-parametric statistical hypothesis test called Kruskal-Wallis Test for assessing whether or not one of the three samples of independent observation tends to have larger values than the other and Mann-Whitney U Test to investigate which strategies in which groups are distinct and the findings revealed that all respondents showed significantly different behaviors to express complaints in the different situations. Moreover, sex and social power were found to cause differential use of complaint utterances.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".