Shopping mall as third place for senior citizens: A case history of Place Versailles Mall in Montréal
Bibliographic record
Abstract
As cities are growing the number of senior citizens are increasing. In this regard, the World Health Organization proposed a physical accessibility, service proximity, security, affordability and inclusiveness as the characteristics of age-friendly cities. Shopping malls as one of the service source of neighborhood carry an important value for older adults. This indoor public space acts as a social hub for the great number of elderly people. The aim of this study is to reflect the importance of shopping mall in older adult’s social life considering shopping mall as the third place. In this study, the observation of the Place Versailles shopping mall has been conducted to evaluate the usage of the shopping mall by senior residents. The mapping, tracing and tracking have been used for data collection. The results illustrate the older adults usually prefer to spend a lot of time in shopping malls and usually see this space as a gathering place. The observation suggested that the most crowded place is preferable for senior residents giving them the opportunity of socializing passively or actively. This study highlights the social value of shopping mall and the potential of acting both as a commercial and social center for the public, particularly elderly adults
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".