Curatorial Omissions in Virtual War Museum Displays: Uncovering Racial and Gendered Tensions in Canadian WWI Exhibitions
Bibliographic record
Abstract
Abstract This paper investigates how virtual exhibitions curated by the Canadian War Museum (CWM) and the Canadian Centre for the Great War (CCGW) communicate ideas about nationalism and multiculturalism in Canada. Drawing from critical literature on participatory museum spaces, multiculturalism, and hegemony, we examine how virtual war exhibitions, which attempt to create space for marginalized identities, may inadvertently reinforce hegemonic understandings of Canadian identity. Through our analysis of two case studies, we explore how digital curation, while offering visitors a sense of participation, may also sanitize resistant perspectives. This analysis allows us to examine present‐day Canadian multiculturalism as it is communicated in digital museum curations, including its practice of including racialized others on a conditional basis, its weaponization of white feminist narratives, and its continued investments in the history of English and French Canada as universal. We thus examine the way Canadian nationalism re‐makes itself, and how virtual curatorial techniques, which may seem more inclusive, can serve hegemonic ends. This study should be of interest to curators and designers working in digital contexts, as well as scholars studying multiculturalism as it is pictured in museums, media, and other cultural representations.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.028 | 0.020 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".