Changing Patterns of Residential Centrality : Population and Household Shift in Large Canadian CMAs, 1971-1996.
Bibliographic record
Abstract
The research focuses on Canadian CMAs with populations of 500 000 or greater over the period 1971-1996. It uses population density gradients and enumeration of population and household shift to assess changing patterns of residential centrality over the twenty-five year period. Results indicate that all of the CMAs examined have experienced continued outward dispersion, some more so than others. When population change in core and inner-city zones is examined in conjunction with reduced density gradients, only one Canadian metropolitan area, Vancouver, shows indisputable signs of strong recentralization. Three other CMAs, Toronto, Victoria and Calgary, also experience some re-population of their central parts, while Montréal and Québec City are shown to maintain what we call "residual" centrality. However, when recentralization is gauged using household enumeration instead of population counts, all of the places studied show evidence of new housing production in the central city. The answer to the central question regarding residential centrality is thus a mixed one, yes and no. Overall, we conclude that there is a direct link between evolutionary patterns within the national urban System and changes observed in residential centrality. Whatever the measure used, highest rates of recentralization accompany strong metropolitan-wide growth over the 25-year period.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".