Co-designing Inclusive Multi-sensorial Ecologies in Museums and Galleries: A Decade of Change in Australian and Canadian Institutions
Bibliographic record
Abstract
Access in museums and galleries is often created through codes, audits, guidelines or visitor surveys, and considered as an add on, at the end of the exhibition planning. This additive approach to access and inclusion—addressing demands for recognition, respect, and rights within the currently dominant cultural system, not changing the fundamentals of the system—can only create surface level changes. In this paper, I draw upon 10 years of research into access in museums and galleries, to provide insights into how a co-design method can shift this additive understanding of access towards critical access as a methodology. Drawing on Canadian and Australian case studies and my research in the evolution of access across museum typologies (e.g., art galleries, human rights museums, history museums, war museums and sport museums), this presentation will provide best practice examples of co-designing inclusion and access beyond codes, manuals and guidelines. I introduce innovative haptic interventions, new inclusive technologies, and a new approach using co-design as a method, and access as a methodology, to develop a multi-sensorial visitor experience. Co-design has the ability to create a feeling of community involvement and community ownership in museums and galleries, and my use of co-design as a method focuses on abilities not disabilities, employing expertise and lived experience, to create equal access to our cultural institutions.
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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 | 0.025 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.026 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.002 | 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".