Bibliographic record
Abstract
Visually impaired pedestrians experience unique challenges when navigating an urban environment because many cues about orientation and traffic patterns are difficult to ascertain without the use of vision. Technological aids such as customized GPS navigation tools offer the chance to augment visually impaired pedestrians' sensory information with a richer depiction of an environment, but care must be taken to balance the need for more information with other demands on the senses. In this paper, we focus on the information needs of visually impaired pedestrians at intersections, which present a specific cause of stress when navigating in unfamiliar locations. We present a navigation application prototype called CrossingGuard that provides rich information to a user such as details about intersection geometry that are not available to visually impaired pedestrians today. A user study comparing content-rich information to a baseline condition shows that content-rich information raises the level of comfort that visually impaired pedestrians feel at unfamiliar intersections. In addition, we discuss the categories of information that are most useful. Finally, we introduce a micro-task approach to gather intersection data via Street View annotations that achieves 85.5% accuracy over the 9 categories of information used by CrossingGuard.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.113 | 0.042 |
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".