Chinatown’s Key, Built Form Elements of Sense of Place: Findings from an Immersive Visual Survey
Bibliographic record
Abstract
Abstract Cities are made up of multiple diverse cultural areas that are rooted in heritage. These areas evolve over time experiencing pressures for change and redevelopment that can erode their sense of place and perpetuate their marginalization. Chinatown in Calgary is one of those areas with strong historical roots and under redevelopment pressure. An immersive, web-based public engagement process using 360-degree photography was used to measure the community’s perception of the importance and impact of built form elements on Chinatown’s sense of place. The built form elements prioritized included streetscape design and programming with a preference for pedestrian over vehicular traffic reflecting its historical use; the buildings’ architectural language with a complexity of motifs, and the use of traditional materials and construction technics that evoke its original culture; buildings respecting human scale; street-wall permeability; and signage as a visible reminder of the original languages. The findings of this study contribute to the understanding of how the built environment contributes to the sense of place in ethnic and heritage areas. They form the basis for urban design guidelines and contribute to more appropriate redevelopment proposals. Helps build inclusivity and a degree of certainty for the future of marginalized ethnic areas of our cities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".