Placemaking for the pedestrian: generative design & the street
Bibliographic record
Abstract
This paper evaluates the influence of placemaking design concepts and historical development processes on the pedestrian experience of three different neighbourhood \ncommercial streets in Toronto. Placemaking is an urban design movement that proposes walkable, pedestrian-‐oriented environments in reaction to Modern planning's "placeless" car-‐dominated landscapes. To explore if placemaking design theories relate to actual pedestrian experiences, I conducted qualitative case studies of three Toronto streets using spatial analyses, interviews, photographic observation, and archival research. I found that placemaking design concepts did correlate to positive pedestrian experiences, but the theories were lacking when it came to explaining pedestrian accounts of the age and storied character of the street. This led me to investigate the historic formation and evolution of each street, where I discovered that, more than conceptual design theories, changes in the scale of street design practices—particularly involving techniques of construction and finance—have played a key role in shaping pedestrian experiences. My findings support Christopher Alexander's process-‐based design theory, and the critical spatial theories of political economy. In my conclusion I suggest some ways that these theories might dovetail into a more holistic approach to design based on adaptive, egalitarian processes. My research shows that consideration of process is essential to any urban design theory that aims to account for the pedestrian experience of streets.
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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 | 0.003 |
| 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 | 0.019 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".