Pixels and personas: Exploring immigrants’ linguistic identity in gaming literacy
Bibliographic record
Abstract
In light of increasing immigration trends, understanding the educational implications for ESL classrooms is crucial. This study underscores the often-overlooked role of gaming literacy within the broader multiliteracies framework, offering insights into its potential to support immigrant integration while preserving linguistic identities. By delving into the interplay between gaming literacy, immigrant identity, and sociolinguistics, ESL curriculum development and inclusive practices within immigrant communities should occupy a significant place in research (Cummings, 2000). These findings hold significant implications for addressing linguistic barriers, reshaping ESL pedagogy, and promoting digital literacy to foster inclusion and belonging among immigrant populations. This research investigates how video games influence identity formation and linguistic adaptation within English-speaking host societies by exploring the intersection of gaming literacy and immigrant integration. By addressing the research question, "How does gaming literacy influence identity formation and integration processes among immigrant communities, and what role does it play in shaping their cultural and linguistic adaptation within the English-speaking host society?" the aim is to uncover how gaming literacy may serve as a tool for preserving linguistic identities while facilitating integration. Examining the benefits and drawbacks of video games for ESL students, their impact on linguistic and cultural identities, and their integration with other digital literacies is to elucidate how gaming literacy can enhance language learning and foster a sense of belonging.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".