Curbing Enthusiasm: Examining Canadian Cities’ Proactive Responses to Evolving Curbside Pressures
Bibliographic record
Abstract
Transportation planning is increasingly concerned with the role the curb plays in urban environments. As the primary boundary separating mobility, accessibility, and amenity activities along a street, it is a highly contested interface between all three. In recent years the number and diversity of curb uses has exploded, placing even greater pressure on this facility. Traditional activities such as on-street parking are being squeezed by new mobility services, e-commerce deliveries, infrastructure supporting shifting transportation modes, and outdoor dining and amenity spaces, to name a few. New innovations such as autonomous transportation will likely further disrupt this mix of uses. As municipal policymakers struggle to keep pace with rapid changes at the curb, some are turning to a new, proactive approach: curbside management. By streamlining curb governance, establishing curb user priorities, incorporating flexibility of uses, and monitoring curb performance using new technologies, curbside management promises to improve outcomes for a greater number of street users. \nResearch of curbside management often focuses on specific policy aspects or technologies, but very few studies to date have helped municipalities assess their current practices or understand where a holistic approach could enhance their existing curb-related governance activities. These are important aspects to understand because many competing private curb interests do not necessarily align with the public interest for this equally public resource. \nThis thesis examines academic literature and practitioner guides related to curbside management to develop a best practice-based evaluation framework. This framework enables cities to examine their curb-related dimensions of governance, policy, organizational structure, and performance monitoring. Its primary intent is to help cities better understand how they presently manage their curb and determine where their greatest potential for improvements lie. The framework is applied to five case study cities in Canada—a context examined very little in curbside management research to date. Findings from these case studies reveal that organizational and policy integration, cost-effective curb utilization data collection, and streamlined by-law environments are areas with the greatest potential to improve management of the curb in Canada’s urban areas.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".