Automatic consequences: A qualitative analysis of suburbanization in Edmonton, Alberta
Bibliographic record
Abstract
Background: The City of Edmonton estimates that its population will double in the next few decades. However, more than 90% of Edmonton residents currently live in suburban spaces that do not support active transportation behaviours, which the federal Public Health Agency identifies as an important contributor to population levels of chronic disease in Canada. Will Edmonton continue to house its new residents in such spaces? Methods: Qualitative analysis of interview data from conversations with urban planners, all employed at the City of Edmonton. Results: The participants characterized Edmonton's history as one of constant suburban expansion. Some expressed hope that Edmonton's future may be a denser, more compact built form while others thought that such an outcome is unlikely. They identified Edmonton's property market as the main determinant of the city's built form, highlighting consumer demand and developers' investment decisions as important processes. Participants indicated that they understood suburbs' negative outcomes with respect to chronic disease, climate change, suburbs' heightened burden on the municipal tax base, and reduced public transit quality. However, they expressed reservations about the effectiveness of city policies to curb suburban growth, citing consumer demand, the 'inertia' that suburban development has in Edmonton, and the involvement of development companies in the workings of local politics. Conclusions: Edmontonians' cultural preference for suburban housing appears to be a strong driver of suburbanization. The state's investment in homeownership and automobile infrastructure contributes to the process. I argue that Edmonton's ongoing suburban expansion is an unavoidable consequence of the city's profit-driven framework of housing provision.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".