Democratizing Museum Practice Through Oral History, Digital Storytelling, and Collaborative Ethical Work
Bibliographic record
Abstract
The museum as an institution can trace its origins to the colonization process. Many are still undemocratic and exclusionary institutions by nature. This article explores how digital collections, digital storytelling, and ethical guidelines for museum professionals working with historically marginalized communities can contribute to democratize museum practice and theory. Making use of two case studies: 1) the creation of the Canadian Museum for Human Rights’ (CMHR) oral history collection; and 2) the planning of the Swedish Museum of Movements’ (MoM) ethical guidelines – this piece proposes a shift from theory to practice in human rights museology to help institutions be more attuned and responsive to the communities they intend to serve. Both case studies demonstrate that implementing human rights museology in national museums is not an easy task and still faces multiple challenges. Yet, they also indicate that this concept can be more productively informed through practices developed by the marginalized groups which have been historically excluded from taking part in the decision-making processes in museums.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".