(De)Constructing Canada: The use of museum spaces in disrupting settler narratives of Canadian identity
Bibliographic record
Abstract
This thesis explores the use of museum spaces in disrupting settler notions of Canadian identity. By identifying the ways in which multiculturalism is written into historical narratives of Canada, these chapters address how the inclusion of a multicultural presence helps curate a Canadian national identity. Drawing on discourses of multiculturalism, race theory, nation-building and some aspects of visual culture, this thesis identifies the ways in which museologies allow for these narratives to be constructed and reconstructed through specific examples within museums. Through the identification of various tropes in the construction of a Canadian national identity – relationship to land and territory, immigration, the nation's colonial past/present – these chapters focus on Indigeneity, whiteness, and anti-Blackness in Canada as a means of addressing the emergence of these tropes, and how these themes are represented within museum spaces.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.038 | 0.021 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".