Bibliographic record
Abstract
The present study aims to compare complaint realizations of Moroccan learners of English (MLE) with American English speakers (AE) and Moroccan Arabic speakers (MA) through an interlanguage pragmatic analysis. The study is carried out with reference to the degree of directness. The study involves 135 subjects: 108 of them are Moroccan students from Faculty of Arts and Humanities of Ibn Toufail. Kenitra. 45 MLE participants were recruited from the English department as the second group of informants, while the 45 MA group were recruited from the department of History and Geography. The 45 American participants included some volunteers from American Peace Corps Morocco and students from Duke university, North Carolina. A written discourse completion test/ task was administered to the participants both native and EFL learners in order to elicit their complaint speech act productions through five hypothetical complaint situations. Responses of Moroccan EFL learners were reviewed to verify whether they approach native speakers complaint norms or Moroccan Arabic norms in terms of directness. In the analysis of the data, all responses were categorized according to Trosborg’s (1995) complaint speech act set. The results show that Learners of English in higher education do not possess the desirable norms of complaint strategies as compared to native speakers of English. Additionally, MLEs exhibit pragmatic transfer from Moroccan Arabic in their use of high complaint strategies. The study ends up with a series of suggestions and recommendations that aim to enhance linguistic and cultural understanding of the target language.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".