Intersectional Approaches for Supporting Casual Language and Culture Learning in Immigrant Families
Bibliographic record
Abstract
In multigenerational immigrant families, everyone is a lifelong learner. Grandparents must learn to foster social connection with their grandchildren despite language and culture barriers, while grandchildren seek to learn their heritage language and culture to better connect with their grandparents. Educational support tools for this context are sparse as language learning apps are often Eurocentric in design and do not fit in the existing routines of immigrant families. In my research, I design educational tools for marginalized immigration populations, such as apps for learning language, preserving family stories, and sharing culture. I design for people at the margins, which requires interdisciplinary intensive approaches. After providing an overview of my research, I present my ongoing project to build a storytelling tool for immigrant families called CrossRoads. CrossRoads employ human-centered approaches that are better suited for uncovering the needs and practices of marginalized immigrant populations. I design and evaluate a tool that fits within the routines of immigrant grandparents and grandchildren by employing the familiar activity of storytelling. I discuss preliminary findings, and implications of my work in the development of educational technology for supporting marginalized, lifelong learners in casual contexts.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".