Cultural bridges in immigrant homes: Jamila Mohammed's family preservation of identity through the "Coffee Room"
Bibliographic record
Abstract
In this thesis, I investigated how immigrants preserved their heritage and developed their identity in the diaspora through cultural objects, such as handmade baskets, coffee rituals, and memories. My research centered around the coffee room in the home of Jamila Mohamed, an Ethiopian immigrant living in St. John's, Newfoundland. I explored Jamila's motivations for designing the coffee room, which resembles those in her hometown, Harar, and uncovered deeper meanings of the room for family members. My research focused on the role these cultural rituals play in a person's everyday life, differing from previous studies on the Ethiopian diaspora which examine how traditional coffee rituals help strengthen ties within communities. I investigated how the coffee room served as a cultural bridge, connecting Jamila and her children to Harar and protecting them from feeling disconnected from their homeland or isolated in their new country through folkloric theories of material culture, performance, and gender. Through the coffee room, Jamila and her family practice their rituals together, as well as share them with non-Ethiopian friends. They strengthen their connections to their new community in Canada, not by abandoning or losing their culture, but through pride by actively participating and adapting their heritage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".