Digital Heritage and Its Implications for Global Citizenship Education
Bibliographic record
Abstract
Digital media technologies provide endless possibilities for disseminating and preserving cultural heritage, opening new avenues to intercultural exchange. Digital-heritage applications have the potential to bring global communities together by providing creative opportunities for cultural exchange and enabling diverse forms of interactions in a culturally specific context of digital-heritage representations. The article presents an immersive experience based on Egyptian heritage developed in a multidisciplinary graduate specialization program at the University of British Columbia Okanagan, Canada. We propose using an educommunication approach to heritage in which students and teachers act as partners to explore new ways of transformative cultural-learning experiences to contribute to intercultural exchanges as implications of global citizenship education (GCED). We investigate how integrating digital-heritage experiences in citizenship education and collaborative student-centered instructional design can promote GCED and enhance intercultural dialogue in university classrooms. Finally, this article aims to fill the gap in the limited literature that connects heritage education and global citizenship and advance the understanding of the implications of virtual reality and interactive 3D-multimedia in taking forward GCED’s mission of promoting intercultural communication, open-mindedness, mutual understanding, and respect.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.000 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".