The deadlock of technocratic planning: Quebec City’s urban form and transportation dilemmas
Bibliographic record
Abstract
In 2010, the Quebec City municipal administration published a sustainable transportation policy. The main recommendations were to increase the use of public transit in addition to more pedestrian and cycling movements for the coming decades. Such objectives were congruent with the city’s commitment to reduce its greenhouse gas emissions and to combat climate change. In a city where 74 percent of daily trips rely on the automobile, what are the potential and limits for the planning of alternative modes in regard to the spatial structure of the city? This article explores how space syntax analysis helps first to understand the main traffic patterns and, second to explain how the road network of the metropolitan area is tailored to serve the automobile. The introduction of sustainable transportation modes, like a tramway service, bicycle paths and pedestrian trips, in order to be competitive, would require finer and more comprehensive planning than the conventional engineering methods applied locally. The spatial analysis, by considering both scale and choice with Depthmap, reveals the importance of the spatial structure regarding daily commuters’ transportation preferences. The Greater Quebec City area covers contrasting historical and contemporary urban fabrics with a layering process of superposed infrastructure improvements. The extensive post-war motorways system is an effective and symbolic statement of a planning strategy centred on the infrastructure. The space syntax analysis offers a more comprehensive framework, closer to sustainable development principles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".