Ethics and Visual Representation
Bibliographic record
Abstract
How do we Ensure Art Accessibility for the blind and visually impaired? This research looks at ways to enhance art accessibility for the visually impaired (V.I). There are not many museums, galleries, exhibitions that provide programs or even exhibit art made to touch. It will examine Art Education for the Blind, Inc. (AEB) founded by Elisabeth Salzhauer, whose mission is “to make art, art history, and visual culture accessible to people who are blind or visually impaired. ” Other sources to be examined are art museums such as Kelowna Art Gallery in Canada, Metropolitan Museum of Art in New York City, and the Museum of Modern Art (MoMA), that each provides such accessibility via Braille labels, large-print booklets, touch and audio tours, and AEB’s tactile representation. An article titled Art for the Blind-Art a GoGo by Kathleen Lang speaks about these programs and talks about how AEB program works. This paper will also study artists such as Roy Nachum and Lee Brozgol. Nachum is an artist who incorporates poetic messages in Braille into his oil paintings as a way for the visually impaired to appreciate his work. Brozgol teaches ceramics to a group of students with visual impairments ranging from high partial to full vision loss. I would like to interview a visually impaired individual to get their experience, if any, in an art museum. I will ask questions pertaining to ways that they would feel more welcome in an art setting. There are some V.I. who are also hearing impaired, therefore I will examine ways to incorporate other senses in art form.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.058 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".