Bibliographic record
Abstract
This article reports on the shopping experiences of people with visual impairment (n = 7) and offers an alternative way to understand their needs. Our study adopted taskscape theory and multiple-method ethnographic perspectives to obtain viewpoints of shoppers with visual impairment and examined shopping activities through two lenses (wayfinding and signage) to determine criteria for improved design. We used taskscape theory to gain insights into how this population perceives signage as well as a participatory, human ecological, systems approach to identify the complexity of wayfinding among people with visual impairment. We used observation, notetaking, photography, and interviews to gain insights into personal and social factors affecting participants’ experiences when navigating in shopping malls. Our data-driven results include a characterization of seven activities—pre-shopping, traveling to the mall, mall navigation, in-store navigation, merchandise evaluation, checkout, and post-shopping—within the shopping taskscape of shoppers with visual impairments that help assess user needs regarding signage and wayfinding. The shopping taskscape provides a systems approach to advance ideas around designing complex environments for able-bodied people and those with disability.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".