Extent, Location and Profiles of Continuing Gentrification in Canadian Metropolitan Areas, 1981-2001
Bibliographic record
Abstract
This study looks at the changes in 10 Canadian CMAs between 1981 and 2001 and builds on the work of other Canadian researchers to show how the extent, location and nature of gentrification processes have continued since the 1970s. The analysis of census data from 1981 and 2001 identifies gentrifying tracts and compares them with the neighbourhoods that are recognised by local housing market analysts as gentrifying and with the neighbourhoods that are clearly not gentrifying. Gentrification between 1981 and 2001 involves between 5 and 12 per cent of all tracts in the CMAs and about 25 per cent of inner-city tracts depending on the CMA examined and the definition of gentrification used. The spatial pattern of tracts gentrifying between 1981 and 2001 is more extensive than areas known to have gentrified during the 1970s. The extent of gentrification does not vary by city size. The profiles of gentrifying tracts show large increases in their proportion of young adult households, dramatic reductions in household size, rapid increases in university educated population, and had more mobile populations between 1981 and 2001. The gentrification of the inner city reduces population density while increasing dwelling unit density. Gentrification in Canada is changing the composition of the inner city but is not repopulating the inner city and it is contributing to the overall decentralisation process in Canadian cities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".