The Metropolitanisation of Canada. Why Populations Continue to Concentrate in and Around Large Urban Centres and what it Means for Other Regions
Bibliographic record
Abstract
Canada’s geography and \nhistorically resource-based economy has given rise to a more dispersed settlement \npattern than in most other nations (certainly, compared to most Western European \nnations) with a broad spectrum of urban areas of various sizes, often located at great \ndistances from each other. The resource-based economy often produced urban \nsettlements whose sole reason for existence was the exploitation (or primary \ntransformation) of a particular resource, be it fish, wood, wheat, hydro-electrical power, \nminerals, natural gas or petrol.However, as elsewhere, Canada’s urban system has come to be dominated by a \nfew large metropolitan areas, home to the chief financial, corporate, and cultural \ninstitutions. Some 37% of Canada’s population lived in the four largest metropolitan \nareas (CMAs) in 2001: Toronto, Montreal, Vancouver, Ottawa-Hull, each with a \npopulation of over one million. It is not impossible that one day close to 60% of Canada’s population will reside \nin a few large metropolitan areas (with populations over 500,000). This gradual \npopulation shift towards large metropolitan areas is a reflection of Canada’s changing \neconomy. As Canada’s economy becomes less and less dependent on resource \nexploitation, its structure is coming to resemble that of other industrialized nations, increasingly dominated by tradable services and high value-added manufacturing.Canada’s economic geography is undergoing a slow but steady \ntransformation as its economy is progressively organized around a few large urban \ncentres. This is what we mean by the metropolitanisation of Canada. The transformation \nis not limited to the concentration of economic activity in metropolitan areas (CMAs) as \nsuch, but also in communities (of various sized) near the largest metropolitan areas.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".