Understanding people’s needs in a commercial public space
Bibliographic record
Abstract
Adapting public spaces for persons with disabilities can be both physically and socially challenging. The two pilot studies presented explore the existing physical conditions of the mall and the social experiences of the mall users as these are documented and experienced by them. The research goals include understanding the physical characteristics of the mall, how access happens, what people experience in real time when going to the mall and what this might mean in terms of issues of social construction of space and personal lived experiences. In both pilot studies, the methods included visual documentation and content analysis of the existing spaces and their design, followed by live in-mall walk-abouts with participants, during which narratives of the experiences were recorded. Researchers engaged collaboratively with participants to understand the experiences, challenges and situations they experienced. Participants include persons with reduced vision or severe vision loss and persons in motorized wheelchairs. Results reveal issues of lack of accessibility, poor contrast and issues of way-finding. Social stigmas add to mall experiences and participants nevertheless reveal the value of the social experiences despite the mall elements hampering their access.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".