Decolonizing or Changemaking: Professional Perspectives on Decolonizing Museum Practice in Small- and Medium-Sized Public Art Institutions
Bibliographic record
Abstract
Ongoing calls for change in the museum sector, paired with recent events, have increased the urgency for decolonization. While past scholarship on decolonizing museums has been dedicated to large-scale institutions, this study focuses on small- and medium-sized public art institutions to ask how they have been responding to calls for decolonization. It uses semi-structured interviews with staff and volunteers to determine how professionals understand decolonizing; how they have been enacting decolonizing in practice; and what challenges have limited decolonial change. Answering these questions revealed that professionals choose to describe their practice using more general terms, commonly diversity, equity, inclusion, and accessibility, which better align with their broad changemaking efforts. This language, however, obscures attention to land and privileges a focus on representation. Furthermore, interviews revealed that despite the process-based nature of decolonizing and changemaking, critical reflection has not been considered an essential component of current practice.
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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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.048 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".