Gender Differences in Online Identity: A Linguistic Contrastive Study of Arabic and English Screen Names in the Saudi Context
Bibliographic record
Abstract
The considerations behind choosing pseudonyms over the real name may be varied depending upon extraneous or intrinsic circumstances including the need to circumvent social norms, taboos, and practices. However, one that transcends these bounds is the affordance of freedom to act natural that comes with adopting a pseudonym which masks one’s true identity. The vast choice of media for social expression in the age of technology has added a new dimension to the practice of adopting pseudonyms. Accordingly, this study investigates whether patterns of screen name choice and typology are discernible among Saudi male and female students. The study created a database of two hundred screen names selected from the forums of foundation year at two Saudi universities (male =100 and female =100). The screen names gained are classified and examined based on the attraction theories’ framework. As far as findings are concerned, the choice and typology of screen names according to the type of gender are significant. The screen names are varied whether the gender is male or female. Findings also show that the "real names" category is applied by female students in screen names more than male students whereas "unreal names" category is applied more by male students than the females. For fictional names, both male and female students prefer to use romantic names and neglected names which show wealth or looks. Three new categories in pseudonyms are found and established in the study i.e., popular, romantic, and real names due to the Saudi contextual variation. A significant effect of choosing the screen names on the names of their devices is also found in the study.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".