An Incomplete Glossary of Change to Activate Decolonising and Indigenising Practices in Museums
Bibliographic record
Abstract
In this article, drawing on my perspective as a settler of white Euro-Welsh/English/Irish ancestry, I discuss words and concepts that are crucial to decolonising and Indigenising museums, with a particular focus on the lands now known as Canada. Museums, heritage spaces and other memory institutions are only beginning to grapple with decolonising and Indigenising approaches that place unacknowledged and unstated colonial norms under scrutiny (despite calls for such actions from Indigenous scholars, curators and activists for many decades if not centuries). The decades of genocide attempts documented in the Truth and Reconciliation Commission’s Report and Calls to Action (2015) amplified the need for this work. Conversations around what ‘Reconciliation’ means for non-Indigenous people are slowly gaining momentum as museums, and the wider GLAM sector, look at how to implement decolonising and Indigenising actions in meaningful ways. I discuss Nerida Blair’s concept of Lilyology and la paperson’s institutional internalisation of scyborgism as part of my discussion of how museums and museum professionals can undertake actions for decolonising and Indigenising their practices and collections.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".