Bibliographic record
Abstract
This article examines how cultural institutions in the Greater Toronto Area (GTA) and nearby regions engage with themes of migration and diaspora through curated exhibitions. Drawing on nine case studies analyzed as part of the Curating Diasporas Initiative (2024), the study explores how community museums, heritage centres, and faith-based archives construct narratives of displacement, adaptation, and identity. While these exhibitions foreground resilience, collective memory, and community building, the article argues that they often follow a linear trajectory (departure, arrival, and integration) that anchors migration and diasporic identities within the boundaries of the Canadian nation-state. Using theoretical frameworks from migration studies, diaspora studies, and memory studies, the analysis is organized around four thematic clusters: Displacement and Migration, Refugees and Religion, Trauma and Belonging, and Architecture and Heritage Building as Migration Narratives. Through these categories, the article identifies shared curatorial strategies, such as the use of personal artifacts, oral histories, and architectural space, while also noting divergences in how trauma, faith, and transnational memory are framed. The conclusion calls for an increased attention toward dynamic, multidirectional curatorial approaches that reflect the ongoing and relational nature of diasporic experience. By examining how institutions both shape and are shaped by multiculturalism in Ontario, this study contributes to critical museology and diaspora scholarship, urging institutions to engage more deeply with the fluid and contested terrain of diasporic identities.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.018 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".