Building Social Presence for Transnational Families Through Mixed Reality Shared Experiences
Bibliographic record
Abstract
Sparked by the frustrations experienced in transnational family communication and inspired by an interest in exploring the potentials of a mixed reality (MR) future landscape, this study investigates the primary research question: how can we use mixed reality to build social presence for transnational family communication? This study reviews literature and contextual works from relevant fields, including presence and social presence, mixed reality, transnational relationships (inter-family and human-space relationships), and technology for social presence for transnational families. Utilizing the Research through Design methodology for iterative prototyping and paired user testing methods, this study explores the use of social MR platforms for transnational family communication. It describes four MR prototypes, paired user testing, and user responses from a live exhibition. This study contributes to theory at the overlapping fields of social presence, mixed reality research, transnational family relationship, and human-space relationship. The mixed reality prototypes, design frameworks, and evaluation criteria for designing mixed reality spaces to build social presence for transnational families also provide significance to design practice. Potential audiences are researchers in fields including mixed reality, communications, diaspora, and transnational studies as well as members of transnational families that may benefit from the research insights presented in this paper.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".