Making Art Accessible to All: Co-Creating Multi-sensory Art with Visually Impaired People
Bibliographic record
Abstract
Blind and visually impaired people experience many limitations when encountering artworks, and most of the general public lack attention and understanding of the visually impaired community. Despite previous research efforts to make visual art more accessible to blind and visually impaired people through audio descriptions, tactile graphics, or digital media technologies, they still face challenges in experiencing art independently and feeling an emotional connection with artworks. This study explores how to create multi-sensory art for blind and visually impaired people to awaken a new form of experience. The study conducted semi-structured interviews to understand the experiences and perspectives of curators and blind artists on multi-sensory art. At the same time, by analyzing two case studies on co-creation with the visually impaired community, this study explores practices of involving the visually impaired community in the creative process. In addition, this study aims to investigate the potential of multi-sensory experiences to enhance the enjoyment and accessibility of art and culture for the visually impaired community. This study will broaden the knowledge about vulnerable communities by exploring the possibility of the visually impaired community as co-designers in multi-sensory art. This knowledge will benefit galleries, museums, and disabled communities and may lead to a positive reconsideration of the importance of an expanded sensory culture in our society.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.021 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".