The Sensational Museum’s Art of Multisensory Storytelling
Bibliographic record
Abstract
Abstract The Sensational Museum, a UK-based, multi-institution research project funded by the Arts and Humanities Research Council (AHRC), aims to challenge ableist biases in heritage sectors. The project explores how the potential of ‘multisensory’ can be leveraged to create inclusive, equitable experiences for both museum professionals and visitors. Led by the concepts of disability gain, equity, and inclusion, the project argues that no one sense should be necessary or sufficient to have rich and meaningful experiences with history and heritage. In this audio recording and descriptive transcript, Sophie Vohra and Charlotte Slark discuss their research for The Sensational Museum, and the value and impact of multisensory storytelling in their work. Using a drum from the Africa Museum as a reoccurring talking point, they expand on the complexities of shifting mindsets and practices to provide more inclusive, progressive and equitable multisensory encounters with museum collections. With insights from Canadian-based professional audio describer, J.J. Hunt, they explore how multisensory language can provide nuanced, rounded, and enhanced descriptions of museum collections and interactions with them. Moving to explore how multisensory storytelling can be embedded in interpretation and communication, Vince Dziekan shares how we can apply his ‘body, mind, soul’ framework to explore multidimensional ways to shape museum interpretation for visitors to meaningful connections with the collections. Overall, they demonstrate how multisensory storytelling can be applied to collections and communication and highlight the important role multisensory language and interpretation have in making museums more accessible, equitable and inclusive.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".