Pragmatic Failure of Turkish EFL Learners in Request Emails to Their Professors
Bibliographic record
Abstract
In an established convention regarding the e-mail communication setting, the e-mails should be linguistically polite to facilitate interaction by reducing the likelihood of conflicts and preventing pragmatic failure regarding the comprehension of any meaning conveyed by what is stated. These are potential problems for most English-as-a-foreign-language (EFL)/English-as-a-second-language (ESL) learners. Therefore, it is the aim of this study to investigate the issue in Turkish EFL context (i.e. the English request emails of Turkish EFL university students to their non-native professors). Specifically, the extent of directness used and the extent and nature of lexical modification employed by Turkish EFL students to mitigate their requests were examined by using authentic data. The data is a part of natural e-mail corpus of 34 Turkish EFL students’ e-mail requests to their two non-native foreign professors over a period of 2 months at English-medium University in Turkey. First, the corpus was coded via coding schemes, (see Appendices A and C) and ranked with a rubric (Appendix B). The results indicated that the Turkish EFL students’ e-mails involved a) direct strategies rather than conventional indirect strategies, b) overusing direct questions and ‘want’ statements, c) underused query preparatory questions, d) insufficient mitigation causing directness and impoliteness, and e) inappropriate greetings and closing statements affecting degree of direction. It is implicated that e-mail instruction (to recipient in various degrees) should be included in EFL books and curricula.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".