“The city before the city”: Attempts at unravelling colonial violence in Canadian museums
Bibliographic record
Abstract
We examine if and how museums contribute to unravelling the fabric of settler colonialism in Canada and take them into view as institutions of the colonial education system: schools, universities and museums play key roles in constructing and spreading certain collective narratives and images, and in silencing others. While all states employ “imagined communities” to legitimate and reproduce the existing order, in colonial states this often includes images and narratives that emphasize the greatness of the colonizers as bringers of civilization. The violence perpetrated by colonization is usually left out. This is the case in Canada as well and is in stark contrast to the epistemic and structural violence that became established with colonial settlement. Our research investigated Canadian universities and museums’ efforts in working through their share in this colonial power system. Being key agents in general education, they select what and whose knowledge is in- or excluded but also have the potential to address conventionally learned misconceptions or distorted images. Canada started its official journey towards “reconciliation” in 2015. We ask whether and how museums take this up: What are they contributing to the declared effort to tackle the colonial system – of which they are a part? Results are presented from an online analysis of how universities and museums across Canada engage in communication strategies surrounding coloniality before we zoom in on museums and focus in more detail and in comparison, on how the Museum of Vancouver created a special space to grapple with the situation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.040 | 0.023 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".