Built environment quality via community & crime prevention lenses: Critical examinations of selected Calgary Light Rail Transit (LRT) Stations
Bibliographic record
Abstract
Calgary’s Light Rail Transit (LRT) system stands as a vital component of the city’s transportation infrastructure, connecting various neighborhoods and facilitating urban mobility. However, ensuring that LRT stations meet the evolving needs and preferences of the community requires continuous assessment and improvement, especially in light of emergent challenges and mounting problems.In this study, we undertook a comprehensive analysis of the design and planning quality of Calgary’s LRT stations, leveraging insights gathered from community feedback channels, including social media platforms, websites, and comments within news reports. The research methodology involves systematically collecting and analyzing comments, opinions, and critiques shared by residents, commuters, and other stakeholders regarding their experiences with LRT stations across Calgary. The research considers the full light rail network, while concurrently focusing more in-depth on two cases (one outdoor and one indoor station). Through sentiment analysis and thematic coding, we categorize and interpret the diverse range of community feedback to identify recurring patterns, concerns, and suggestions related to station design, accessibility, amenities, safety, and overall user experience.The potential outcomes of this research hold significant practical implications for researchers, architects, planners and politicians alike. For researchers, the findings offer valuable insights into the public's perceptions and preferences regarding LRT station design, aiding in the formulation of evidence-based recommendations for future infrastructure projects. Architects can use this information to refine their designs, ensuring that LRT stations are not only aesthetically pleasing but also functional and responsive to community needs (including the import of perceptions and the value of lived experience). Planners and politicians, on the other hand, can utilize the insights to inform policy decisions, shape the regulatory milieu, and prioritize improvements that enhance the overall quality, accessibility and functionality of Calgary's LRT system, thereby fostering sustainable urban development while improving the commuting experience for residents and visitors alike.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".