Wayfinding Experience of Persons with Autism Spectrum Disorder within a Museum Context
Bibliographic record
Abstract
Designing for disabilities may present great challenges but the potential rewards that result may benefit a wider population.This study investigates whether the wayfinding available within a specific museum setting accommodates the needs of persons with Autism Spectrum Disorder (ASD).Identifying the 'wayfinding' needs and/or preferences of the group that was studied in this research is an important first step in developing a knowledge base to further this area of research and perhaps assist designers to facilitate more inclusive wayfinding experience within museum settings.To obtain some insight on the personal experience of people with ASD, three qualitative methods were used: an anonymous survey to gather basic insight about museum visit experience showing that most respondents do visit museums and the majority do not use any assistive devices during their visits; an observational study revealed how participants navigated this particular environment and that visual memory played a large role when traveling through the museum; and finally, a workshop revealed environmental preferences and aversions highlighting the differences between individuals, and how an understanding of diversity is an important consideration in design.The findings of this case study support the notion that it is challenging to design a space to support a diversity of needs and preferences, but that improving our knowledge of diversity as well as commonalities can better support the movement and aspiration toward universal design.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".