"Good Morning, My Dear Dr." Openings and Closings in University Students’ Academic Emails
Bibliographic record
Abstract
The current study investigates the openings and closings in the emails of Jordanian undergraduate students to their professors. It is significant as it provides invaluable insights into different aspects of student-professor interpersonal communication. This study seeks to fill a gap in the literature by examining how Jordanian university undergraduates open and close their first-contact emails to their professors. This study uses both quantitative and qualitative research methods. The sample of this study consisted of 200 authentic Arabic email messages drawn from a professor’s mail inbox. The data were analyzed based on Salazar-Campillo & Codina-Espurz’s typology of opening and closing. The findings reveal that the emails included all the opening and closing moves reported by previous research, however, with clear variation. The findings also show that openings and closings are used as politeness strategies to create a positive tone for student-professor academic interactions. Moreover, the study concludes that the emails resorted to more informal opening and closing formulas. The emails in this study do not conform to the norms and etiquette of student-professor email interaction. Furthermore, this study reports the use of emojis in almost all moves of the opening and closing sequences. Based on the findings, future studies are recommended.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".