Bibliographic record
Abstract
This article presents a case study of the Ukrainian Museum of Canada – Ontario Branch through its flagship exhibit, Trunk Tales: Leaving Home...Finding Home. The Museum—founded in 1944 with modest means and a single artifact—now operates as a compact but influential institution, preserving the material and intangible heritage of Ukrainian-Canadians across five distinct waves of migration. This paper examines the Museum’s spatial limitations, digital adaptations, and interpretive strategies in curating diasporic memory. Through the lens of the Trunk Tales exhibit, which combines historical artifacts, oral histories, and community narratives, the study analyzes how the Museum navigates themes of forced migration, cultural preservation, and identity transformation. Special attention is given to moments of cultural exchange, particularly between Ukrainian and Indigenous communities, as seen in shared artistic practices. The exhibit also explores the Museum’s response to historical silences, such as the omission of WWI internment narratives, revealing tensions between preservation and erasure. The evolving mission of the Museum—especially in light of the ongoing Russian invasion of Ukraine—underscores the urgency and complexity of diasporic remembrance. Ultimately, this article argues that the Museum functions not only as a container of cultural memory, but as an active agent in shaping the living identity of Canada’s Ukrainian diaspora. By examining its physical and digital presence, this case study contributes to broader museological discussions on space, narrative authority, and the politics of memory in diasporic institutions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".