Arabic-English Code-Switching in the Saudi Video Gaming Community: A Sociolinguistic Perspective
Bibliographic record
Abstract
In this sociolinguistics paper, I discuss code-switching behaviour while playing an online video game. The purpose of the study is to bridge the knowledge gap in the literature regarding code-switching within the Saudi Arabian gaming community. Although a significant amount of research has been carried out on the topic of code-switching, the phenomenon of code-switching among online gamers has received little attention. The focus of this research is Saudi online gamers playing online video games, specifically Overwatch (a team-based online multiplayer game). The research questions investigated how the game format (casual or ranked) and the age of the players influence the occurrence of code-switching. Data collection was based on a quantitative approach and participating in Overwatch matches. Observing the presence of players and their frequency of code-switching allowed for the creation of objective data. The findings indicate that both the format of Overwatch matches and the age of the players had an impact on code-switching. Matches that took place in an intense setting (ranked matches) had more instances of code-switching than those in a casual setting. The results show that the age of the players affected code-switching because younger players were less likely to code-switch than older players were. The research illuminates the ways in which individuals who are part of Saudi Arabia’s gaming community interact with one another and sheds light on the online settings in which code-switching is most prevalent. Future studies should investigate other video game genres to broaden the understanding of the phenomenon of code-switching in online video games.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".