Filtering, City Change and the Supply of Low-priced Housing in Canada
Bibliographic record
Abstract
This paper examines the filtering process and shows the extent of the forces that are gentrifying Canadian cities. The 1996 census micro data are used to develop rent- and price-age profiles of dwellings in each of Canada's census metropolitan areas. The analysis shows that the filtering process is both too slow and, at best, can have too small an effect to be a part of a government strategy for reducing the housing burdens of low-income people. Filtering is not helping lower-income households. The most important finding shows the reversal in the direction of filtering in all Canadian metropolitan areas since 1981. The rents and prices of older dwellings have been rising faster than those of the newer units. The steep 1971 rent- and price-age profiles for Montreal and Toronto are explained to show that government policies would not be able to induce the amount of filtering needed to have a noticeable effect on the welfare of lower-income households. The pressures that eventually find expression in gentrified neighbourhoods are affecting much of the older housing stock. Cities in other countries that are experiencing gentrification may be subject to the same pressures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".