A comparative analysis of wellness in Canadian cities: More alike than different
Bibliographic record
Abstract
The present study examined the Moneysense 2016 rankings of 219 Canadian cities to further test the empirical derivation of Canadas best cities based on 30 indices covering climate, financial information, crime, and wealth as derived from a principle axis factor analysis, and explained 47% of urban rankings: Climate (13% explained variance consisting of amount of rainfall, number of days above 24°C); Financial (12% -- income, net-worth, taxes); Crime (11% -- crime rate and severity); and Wealth (11% -- car and luxury car ownership). A stepwise multiple regression predicted overall urban rank based on 10 significant indices: Rank scores were higher (like place finished in a race, so that worse cities performed worse) when individuals encountered greater unemployment (both in general and within the arts), when individuals drove older and lower quality vehicles, or relied greatly on walking in order to get to work, when household net-worth was low, when housing was less affordable, and when both crime rate and severity were high. Finally, a cluster analysis (examining how cities were similar according to comparable indices) uncovered three different types of Canadian city: Less populated cities; mid to large sized urban centres; and Mega-cities, with populations above 3 million residents, including West Vancouver, Toronto, Calgary, and Montreal. Mid/large cities were typically found in the penumbra of Mega-cities (e.g., the greater Toronto area). Of note, those cities in the largest cluster were more alike than they were different. The examinations into these cities may not explain which cities are populated with the happiest individuals, but it does offer insight into why people will flock to certain cities, perceiving them to be better places to live.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".