Openings and Closing in Emails by CLIL Students: A Pedagogical Proposal
Bibliographic record
Abstract
Email communication is pervasive in faculty interaction. As there exists status imbalance between students and professors in this type of context, emails are expected to cater for the uneven power relationships by means of using appropriate polite features. Previous research (e.g., Eslami, 2013; Salazar-Campillo & Codina-Espurz, in press) has pointed out the pragmatically deficient use of openings and closings in requestive emails sent by non-native students in educational contexts, revealing a lack of the expected deference and respect to the professor. In this paper, we firstly explore this topic further by looking at different groups of emails written by CLIL students to ascertain whether this variable affects students’ pragmalinguistic and sociopragmatic ability when writing their emails. Secondly, a proposal for the teaching of openings and closings is suggested, which aims to help second and/or foreign language learners behave in a more target-like manner thus avoiding pragmatic failure.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".