Multilingualism Between Inclusion and Exclusion: A Social Semiotic Analysis of Invented Languages in Chants of Sennaar
Bibliographic record
Abstract
In an era of digitization, video games constitute an ideal site for examining linguistic and cultural diversity. As one of the important semiotic resources in video games, invented languages create a space for representing different social meanings. This study investigates how invented languages in Chants of Sennaar are ideologically represented through different linguistic and semiotic resources. Findings indicate that invented languages become a site of ideological contestation for social inclusion and concomitantly social exclusion. On one hand, invented languages as an inclusive form of multilingualism can diversify knowledge base, bridge cultural divide and assemble semiotic repertoires. On the other hand, multilingualism can reproduce social inequality in the access to playing the game, consequently recreating homogeneity and socially differentiated practices. By analyzing the semiotic representations of invented languages, this study contributes to the nuanced understanding of multilingual studies by expanding the scope of research inquiry on video games, an under-explored but increasingly prominently research area in sociolinguistics. The study concludes by addressing the sociolinguistic dimensions of invented languages in relation to a broader context of social practices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.007 |
| 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".