Gender Analysis of Writing E-Mails Received from Graduate Students at Saudi Universities
Bibliographic record
Abstract
The relationships students form with faculty play a critical role in their success at university, it can ease students’ fears about seeking assistance on assignments and other issues they may encounter. Therefore, e-mails have become an important part of the educational process. Accordingly, this study aims to explore the impact of gender orientation on the language used by Saudi students writing e-mails to their instructors at Saudi universities. The study depends on Media-Richness Theory (MRT). It intends to stay away from misinterpretation or disarray in cross-gender communications and encourage instructors and students to gain more familiarity with common communication styles in Saudi Arabia. MRT is used to analyze three linguistic features, namely abbreviations, emoticons, and word length. This study utilized a quantitative research design. Considering 24 e-mail samples, 12 were received from male students and 12 were from females. Among male students, the findings indicate that abbreviations are used in their e-mail communications but less often by female students. With regards to emoticons, female students tend to use them more frequently than male students. Lastly, in the case of word length, female students appeared with a significant number of words per e-mail, whereas few male students did.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".