Towards a More Inclusive Museum: Developing Multi-Sensory Approaches to the Visual Arts for Visually Impaired Audiences
Bibliographic record
Abstract
This thesis investigates the contribution of multi-sensoriality to the understanding and appreciation of two-dimensional artworks, such as drawings or paintings, by people with visual impairments.This exploratory study was conducted from a disability studies perspective, in which the researcher's role was that of a facilitator and the participants were experts of their own experience.This participatory action project draws on interdisciplinary research practices in several interrelated fields, including accessibility and disability studies, blindness studies, anthropology of the senses, tactile perception and haptics, and translation studies.Previous research focused primarily on initiatives to translate visual arts into tactile content to make them accessible to people with visual impairments.The present study contributes to multi-sensory initiatives for the low-vision and blind community, drawing on research findings of sensory museology and cognitive psychology to deepen our knowledge about multi-sensory translation.Data collected from three rounds of individual interviews and one co-design session with visually impaired participants was examined using a qualitative methodology.A thematic analysis was developed to inform design decisions and identify barriers to inclusion in art museums.The study also included the co-creation of multisensory translations of Franklin Carmichael's painting In the Nickel Belt (1928) and Outport Icon #2 (2001)(2002) by Duncan de Kergommeaux.The broader findings of this study provide evidence to support the notion that touch and hearing can play a valuable role in conveying the nature of art to visually impaired visitors, giving them greater autonomy in their interpretation.This study challenges the long-standing tradition of museums relying on vision as the only acceptable sense through which visitors can access visual arts.While further work may be needed to assess the degree of equivalence between experiencing a painting through sight and accessing it through touch and hearing, this project establishes a foundation for further research and initiatives pertaining to the implementation of antiocularcentric approaches in museums.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.002 | 0.003 |
| 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".