Public Policy at the Intersection of Market Forces and Social Equity: A Case Study of Gentrification and Redevelopment in Calgary’s East Village
Bibliographic record
Abstract
This study examines to what extent Calgary’s East Village revitalization meaningfully addressed the needs of incumbent low-income residents. Using data from the Canada Mortgage and Housing Corporation (CMHC), Statistics Canada, and the Maytree Foundation, the analysis situates neighbourhood change within broader trends in Calgary’s rental housing market between 2000 and 2024. Rents rose steadily during this period, with marked surges in the mid-2000s energy boom and again after 2021 amid possible migration-driven demand. Vacancy rates fluctuated between 0.5% and 6% (CMHC, 2024), moving inversely with rents and reflecting the cyclical nature of Calgary’s economy. Affordability pressures are particularly visible when comparing average rents to social assistance incomes. Between 2015 and 2023, annual rent consistently exceeded the income of a single individual on assistance by 45–75% (CMHC, 2024a; 2024b) and remained high for those with children or disability. For instance, in 2015, the average annual rent in Calgary was approximately $13,560 (CMHC, 2024a; 2024b), while the total welfare income for a single parent with one child in Alberta was $17,403 (Tweddle, 2016). This means that rent accounted for about 78% of total income. With more than three quarters of income required just to cover rent, very little remained for other basic needs, underscoring the persistent housing affordability challenges faced by households reliant on social assistance. These figures suggest that incumbent resident’s dependent on social supports would have had little capacity to remain in East Village or secure alternative housing within the city as redevelopment advanced. The redevelopment, led by the Calgary Municipal Land Corporation, delivered significant physical renewal and private investment, but was not accompanied by affordability requirements or anti-displacement policies. While the project succeeded in transforming the built environment and attracting new residents, the absence of targeted protections raises questions about who benefitted and who was excluded. The case of East Village highlights the tension between economic revitalization and social equity, leaving open the question of whether incumbent low- residents were considered in planning or ultimately marginalized by market forces.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".