An Exploration of Middle Class Dynamics in Canadian Cities
Bibliographic record
Abstract
A strong middle class contributes to a healthy democracy, economic growth, and political stability. As of 2011, approximately 81% of Canadians live in cities, rendering studies at the metropolitan level essential. This paper provides a large scale and comparative quantitative study focused on the middle class in Canadian cities. Utilizing census data from 1996, 2006, and 2016 at the Census Metropolitan Area and Census Agglomeration levels, I estimate bivariate and dummy variable OLS models to explore middle class dynamics across Canadian cities. Spatially, results appear to show a negative trend westward in the middle class in the 21st century that is largely driven by regional effects of the resource boom. Key findings regarding possible drivers of the differences in middle class shares across cities center around secondary industry and knowledge-intensive business services. Secondary industry has a positive relationship with the middle class, while knowledge-intensive business services have a negative relationship with the middle class. The results of this study seem to fit previous theoretical frameworks such as the deindustrialization and routinization hypotheses relating to the drivers of inequality and provide a starting point for future studies of the middle class.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".