An Urban Nation: The Shifting Fortunes of Canadian Cities
Bibliographic record
Abstract
Canada is not immune to the dramatic economic changes that are transforming society in other industrialized countries, where once-thriving factory and resource towns are dying, while educated knowledge workers in more cosmopolitan centres prosper. Where this growing inequality between communities and social classes takes root, worrisome social and political developments can develop, such as the polarization occurring in the U.S. and parts of Europe. Canada’s 10 largest cities have been the primary driver of economic growthin recent years, and Canada is unusual in the degree to which its population is concentrated in a relatively small number of cities. To date, Canada’s largest cities have been doing well and Canada has not so far seen the contrast so evident in the United States between highly successful cities and large cities in decline, such as Detroit and Cleveland. However, a ranking of national cities using “vitality” scores highlights a growing inequality between Canada’s largest cities and its midsize and smaller cities.In many communities in the Atlantic region, in Quebec beyond its two major cities, and in the northern regions of B.C. and Ontario, harder times may lie ahead. Their populations are stagnating, their employment rates for people of prime working age are distressingly low, and their proportion of low- income families is high. Urban decline can lead to further poverty, significant population aging and more pressure on higher levels of government to provide services that these communities can no longer afford. The strength of cities primarily revolves today around human capital and the ability of a community to develop or attract a highly skilled labour force. If Canada is to avoida future where just a few cities are economic and demographic “winners” and the rest are “losers,” policy-makers will need to consider how to help keep midsized cities from being increasingly left behind, whether that be through diversifying immigration patterns, targeted investment outside large urban areas, or other approaches. The pandemic, which has led some employers to rethink the need to keep workers in expensive big-city downtown offices, could create new opportunities to reinvigorate smaller, lower-cost centres. However, without a change in the pattern of divergence between Canada’s dynamic cities and the rest, the societal and political strife that has unfolded elsewhere could someday happen here.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.034 | 0.007 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".