Towards Inclusion in Museums: Multisensory and Cross-Modal Translations/Interpretations of Visual Artworks
Bibliographic record
Abstract
Access to art and cultural works is a fundamental human right, irrespective of abilities and human differences. However, traditional museum experiences heavily rely on visual perception, which creates barriers for visitors—especially for those who are unable to access art through sight. How can visual art be “translated” into other modalities, and what might be their affordances, limitations, and impact? This qualitative investigation focused on a graduate course on multisensory museum experiences embedded within a unique partnership between the Art Gallery of Ontario and OCAD University. Observations and interviews with students, instructors, museum visitors, and stakeholders (including community members with vision impairments and museum professionals) revealed: a range of translation/interpretation strategies, from “literal” (mapping visually perceived spatial properties of artworks to non-visual perceptual modalities) to “constructivist” (non-literal mappings that aim to engender audience memories that are akin to what might have inspired the original artwork); transformative student journeys, such as building meaningful connections with art; and significant impact on diverse audiences and students. This study revealed promising directions for inclusive museums, a preliminary technical language to support the design of translations/ interpretations, and a need for theoretically informed and tested standards to guide these designs and practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.010 |
| 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 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".