Memory through Design: Supporting Cultural Identity for Immigrants through a Paper-Based Home Drafting Tool
Bibliographic record
Abstract
Current research in HCI with immigrants predominantly focuses on their practical needs and little attention is given to their cultural identities. As such, we aim to understand how newcomers reflect their cultural values within domestic settings. We explore this by provoking memories immigrants associate with physical spaces inside their homes. Hence, we built "Our Home Sketcher": a paper-based home drafting tool that allows novice users to design their homes by sketching and implicitly expressing their space, light, and privacy preferences. The collected drawings are then fed into a computer algorithm that produces 3D models of the sketched houses. This process of design acts as an artifact-driven storytelling for heritage sharing and rapport building within migrant communities. We engage 13 Middle Eastern newcomers in Canada with the tool and use Halbwachs' [44] theory of collective memory to frame how home sketching provokes former experiences. Our findings show a strong longing for reclaiming the past, narrating space-related oral history, and designing beyond current limitations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".