Less Talk, More Builds: The Mixed-Income Residential Tower Model of the University of Winnipeg Community Renewal Corporation
Bibliographic record
Abstract
The search for and replication of scalable models for affordable housing amid North America’s housing insecurity crisis has been frustratingly slow. Governments are flailing—and, so far, failing—as they try to put in place the necessary policies and incentives for the private, public, and non-profit sectors to accelerate the construction of the millions of new affordable units required to confront the crisis. This paper highlights one model whose replication is underway in Winnipeg’s downtown core: that of a mixed-income, mixed-use residential tower offering nearly half of its units at affordable rental prices for marginalized residents and designed, built, and managed with a deep commitment to multi-dimensional sustainability. The catalyst for this initiative is the University of Winnipeg Community Renewal Corporation (UWCRC), a non-profit foundation that works in partnership with community organizations and is now Winnipeg’s leading social real estate developer. The Corporation is the second component of the model. While there are no perfect strategies for solving the housing crisis, UWCRC’s approach deserves to be widely known, deeply studied, and rapidly adapted and replicated at scale by universities, colleges, and other public institutions in urban centres throughout North America. Engaged scholars can play important roles in this effort.
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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 | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".