Public insights into the Plus-15: Perspectives of Calgary's Grade-Separated Pedestrian Network
Bibliographic record
Abstract
Designing pedestrian-friendly environments with a focus on human-centered placemaking is crucial for creating vibrant, inclusive, and safe urban spaces, conducive to promoting wellbeing and health. There is a dearth of rigorous evidence concerning user experiences of Grade-Separated Pedestrian Networks (GSPNs). GSPNs encompass infrastructure including bridges and tunnels designed to improve pedestrian mobility (i.e., convenience), separate pedestrians from motor vehicle traffic (i.e., safety), and protect pedestrians from inclement weather (i.e., comfort). Applying a placemaking lens, we explored user perceptions and experiences of the “Plus-15” – an elevated GSPN in downtown Calgary (Canada). Recognizing that interpretations of quality in the built environment are subjective, context-dependent, and socially constructed, we utilized qualitative descriptions to explore public comments on the Plus-15 posted on the Reddit social media platform. We searched posts from the r/Calgary subreddit from October 2008 to September 2023, extracting comments concerning the Plus-15. Using a placemaking framework, we conducted deductive coding of relevant posts. We interpreted and translated coded comments into descriptive summaries based on 10 placemaking dimensions, including density, land uses and destinations, accessibility, connectedness and permeability, legibility, attractiveness, inclusiveness, maintenance, safety, and character. Our analysis of 28 relevant subreddit posts revealed that Plus-15 users shared a strong connection with its distinctive destinations, invoking feelings of nostalgia and fostering a sense of belonging. Users voiced concerns about the presence of homeless populations affecting the Plus-15’s character and safety. There was positive sentiment towards the Plus-15’s role in enhancing pedestrian connectivity and providing shelter from adverse weather. Users highlighted maintenance issues as adversely affecting their experience and perceived safety. Users also noted that ongoing renovations and the unclear responsibility for network maintenance negatively affected pedestrian movement within the Plus-15. Our findings highlight opportunities for improving the quality of the built environment related to the Plus-15 to enhance user convenience, safety, and comfort.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".