From Oral Traditions to Digital Landscapes: Virtual Reality as a Tool for Revitalizing Indigenous Languages
Bibliographic record
Abstract
This thesis investigates the role of virtual reality (VR) as an innovative tool for Indigenous language revitalization, emphasizing its potential to bridge generational and geographical gaps in language transmission. Centered on the Multimodal Indigenous Knowledge Systems (MIKS) framework, the study explores how VR can provide immersive, culturally rich environments that support language learning and cultural connection. Grounded in the principles of embodied cognition, the research highlights how VR can integrate traditional practices, stories, and landscapes to create engaging, contextually relevant learning experiences. The study examines the perspectives of VR creators, educators, Elders, and youth participants through interviews, focus groups, and workshop data, shedding light on VR's emotional, experiential, and motivational impacts. It also addresses challenges, including technological accessibility, cultural sensitivity, and the need for community-driven development. Findings demonstrate that VR may foster a sense of belonging and cultural pride while supporting language retention and engagement, particularly among Indigenous youth. This research underscores the significance of technology in advancing Indigenous language and cultural revitalization by situating these findings within the broader context of the United Nations Decade of Indigenous Languages and Canada’s Truth and Reconciliation Calls to Action. The results contribute to a growing body of scholarship on digital tools for education, providing actionable recommendations for culturally sensitive VR development and future research in this emerging field.
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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 | 0.002 |
| 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 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".