Gain, Loss, and Change: The Impact of Condos on Winnipeg Neighbourhoods
Bibliographic record
Abstract
Since their introduction the late 1960s, condominiums have become a significant feature of Winnipeg’s housing market. Condominiums in Winnipeg however, present particular characteristics compared to other Canadian cities – especially the high number of units converted from previous apartments, the small share condos represent of the overall housing market, and the low percentage of condos that are rented out. Previous research has identified the high number of rental units converted to condos citywide, and raised concern about the impact on affordable rental housing. This project developed a new comprehensive database to explore in-depth the impact of condo units on Winnipeg neighbourhoods. The result provides a more nuanced understanding of the impacts of condos on Winnipeg neighbourhoods. With this database we set out to answer the following research questions: \n \n● What is the spatial and temporal distribution of condominiums in Winnipeg? \n● How does the distribution of new purpose-built condominiums differ from condominiums converted from other uses? \n● Which neighbourhoods have been most affected and how has the housing stock in that neighbourhood changed? \n● What effect have conversions had on the availability and affordability of the affordable rental stock in these neighbourhoods? \n \nThe key themes emerging from this study are the loss of affordable rental housing, but a concurrent gain in affordable homeownership options. Additionally, the impacts of condos show very strong differences amongst neighbourhoods – in market effects, on rentals, and on demographics
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 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".