Sprawl Repair in Ottawa: Analyzing Facilitating and Impeding Factors of Mall Redevelopment
Bibliographic record
Abstract
As cities face growing pressures from climate change, population growth, and unsustainable land use, suburban malls have emerged as key opportunities for urban transformation. This report investigates the redevelopment potential of two aging mall sites in Ottawa—Carlingwood Mall and the Merivale Road corridor (Emerald and Meadowlands Plazas)—to understand how underutilized commercial properties can be repositioned to support more sustainable urban forms. Employing urban tissue analysis, stakeholder interviews, and scenario-based design interventions, the study evaluates physical, regulatory, and market-based factors that influence redevelopment outcomes in the City of Ottawa. Findings reveal that the Merivale Road site presents stronger near-term redevelopment potential, attributed to its fragmented surrounding land uses, underused infrastructure, and fewer constraints from existing tenants. Citywide, challenges such as long-term leases, infrastructure capacity, market uncertainty, and community opposition continue to pose significant obstacles to mall redevelopment. Meanwhile, evolving policies, shifting consumer preferences, and improved transit accessibility are catalyzing transformation at these underutilized sites. The study highlights the critical role of regulatory frameworks in shaping redevelopment trajectories and calls for adaptable municipal policies, streamlined approval processes, and early community engagement. These strategies are essential in repositioning suburban mall sites as anchors for sprawl repair and more resilient, sustainable urban growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".