Exploring Collaborative Culture Sharing Dynamics in Immigrant Families through Digital Crafting and Storytelling
Bibliographic record
Abstract
Families strengthen bonds by collectively constructing social identity through sharing stories, language, and culture. For immigrant families, language and culture barriers disrupt the mechanisms for maintaining intergenerational connection. Immigrant grandparents and grandchildren are particularly at risk of disconnect. In this paper, we investigate existing design guidelines using a tool (StoryTapestry) to explore the storytelling and crafting process of South-Asian immigrant grandparents and grandchildren. In this exploration, pairs used culturally-relevant images to create digital visual artifacts that tell their stories. Grandparent-grandchild pairs from 10 South-Asian immigrant families participated in this exploration of how the digital process fosters positive social connection, culture sharing, and co-construction. A thematic analysis revealed how collaborative digital crafting encourages the crossing of language and culture barriers, knowledge sharing, and creativity. We contribute an understanding of interaction dynamics and socio-technical implications of intergenerational and cross-cultural collaboration by demonstrating (1) that collaborative digital crafting can reverse traditional educator and learner roles to create culture sharing opportunities, (2) that grandparents play a central role in maintaining social interaction, (3) that structure can guide grandparent-grandchild pairs to a shared goal, and (4) that flexibility encourages engagement from children. We synthesize ideas from migration and collaboration research, and we discuss how the culture, language, and generational dynamics in our study extend what is known about each of these spaces. Together, our design implications offer insight into building digital tools that promote engagement, knowledge sharing, and collaboration between immigrant grandparents and grandchildren navigating social disconnect post-migration.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 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".