Student-Teacher Email Requests: Comparative Analysis of Politeness Strategies Used by Malaysian and Filipino University Students
Bibliographic record
Abstract
This comparative study investigates politeness strategies used in Malaysian and Filipino student-teacher requesting emails. After analyzing a corpus of 40 student-teacher email requests that are written by Malaysian and Filipino university students, it is found that Malaysian university students use more direct (i.e., imperative, interrogative) requesting politeness strategies than their Filipino counterparts while Filipino university students use more indirect (i.e., positive, negative) requesting politeness strategies than their Malaysian counterparts. It is also revealed that Filipino students realize their requests longer with more strategies than Malaysian students as they produced more politeness moves than their Malaysian counterparts. This implies that while Filipino students tend to give more reasons and employ other strategies to justify their requests and appeal to their teachers, Malaysian students tend to directly, but politely using politeness mitigation markers, express the request. This study also shows that the use of negative politeness strategies is prominent among Malaysian and Filipino students; however, students also used positive politeness to establish solidarity with teachers with their offering of explanation and expression of goodwill. Although the findings of this study support the universality of request and politeness strategies, differences between the two groups of students are also proof of the culture-specificity of certain strategies. It is recommended to incorporate cross-cultural pragmatic awareness in language classrooms to encourage students to be more culturally adaptable as they engage in communication with people from different nationalities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".