(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 â relationship to land and territory, immigration, the nation's colonial past/present â 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".