A Design Thinking Approach to Sustainable Inclusive Shopping Environments for Grocery Shoppers with Low Vision
Bibliographic record
Abstract
The seemingly simple daily life activity of grocery shopping could be an exhausting ordeal for people who are blind or have low vision. The objectives of this inclusive design research study were to ethnographically identify the barriers experienced by shoppers with low vision and to carry out participatory design of an inclusive solution to mitigate the situation. This report documents the four phases of the study – Discover; Define; Develop; and Disseminate. A demo prototype ‘Shopping Buddy,’ with the innovative idea of Universal Product Inclusive Code (UPiC) at its core, was designed to aid shoppers with low vision. The UPiC system will provide necessary product information to the shopper by leveraging existing data from the manufacturers’ databases. Thus, the proposed solution is not just an assistive tool; it also includes ingredients for effecting systemic changes in the retail grocery ecosystem comprising the industry’s Manufacturers, Distributors, Retailers and Consumers (MDRC). To make the benefits, and resulting inclusion, sustainable, recommendations regarding the UPiC are made to the MDRC, also addressing the needs of commercial viability. Such sustainable inclusive design will ultimately result in curb cut advantages for all shoppers, thereby enhancing sales and benefitting the Canadian economy.
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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 | 0.022 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".