Tactile Map Creation: Supporting Wayfinding for People with Sight Loss
Bibliographic record
Abstract
Tactile maps are specialized maps designed for tactile perception, providing spatial information through touch rather than sight. These maps are crucial for visually impaired individuals, offering an accessible means to understand and navigate their surroundings. The significance of tactile maps extends beyond mere navigation; they empower visually impaired people with greater independence and confidence in exploring new environments. This capability is particularly vital in urban settings (such as a campus), where complex layouts can pose significant challenges. At Toronto Metropolitan University (TMU) Libraries, we have initiated and built upon an existing innovative process for creating tactile maps, addressing the unique needs of the visually impaired community. This presentation will outline the process for tactile map creation, highlighting the steps involved from data acquisition to the production of the final tactile map. We will also discuss the software used, the analytical methods employed, and the considerations necessary for creating effective tactile maps. Finally, we will propose a focus group approach to refine this process, ensuring the tactile maps produced are not only accurate but also user-friendly and practical for the intended audience. Through this presentation, we hope to share our insights and methodologies, contributing to the broader efforts in making spatial information accessible to all.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".