Challenge of defining a national urban strategy in the context of divergent demographic trends in small and large Canadian cities.
Bibliographic record
Abstract
Once forgotten as an object of research, a growing literature dealing with various aspects of small cities has emerged since the new millennium. The answer to the question "does size matter?" has so far received positive empirical support on both sides of the Atlantic. Using the Federation of Canadian Municipalities (FCM) three quality of life studies as backdrop, this paper offers further evidence that small Canadian cities are worth our attention. Since 1999, FCM has extrapolated results from its series of quality of life studies carried out on a sample of large and medium sized cities to monitor key changes in the quality of life of Canadian urban residents. Conclusions drawn from these studies have been used to define a common Canadian municipal agenda which identifies air pollution, public transportation, affordable housing, homelessness, social inclusion and integration, and community safety and security as some of Canada's key urban policy priorities. Following the evolution of a number of key demographic indicators in larger and smaller Canadian cities between 1996 and 2006, this research questions whether the municipal agenda derived from FCM's quality of life studies offers a fair and just reflection of the reality and of the public policy priorities of smaller urban municipalities.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.018 | 0.019 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".