Culture, Language, and the Location of High‐Order Service Functions: The Case of Montreal and Toronto
Bibliographic record
Abstract
Abstract: Today, there is plenty of evidence of metropolization—the concentration of economic activity, particularly of high‐order services—in the world's largest cities. Furthermore, within most national systems, the urban hierarchy is stable, especially toward the top: cities that were the largest 100 years ago continue to dominate their respective systems today. In Canada, however, this is not the case. Over the past 40 years, there has been a reversal at the top of the urban hierarchy, with Montreal losing its dominance in favor of Toronto. In this article, we document the reversal and elaborate a model that accounts for the spatial shifts in high‐order services. Our analysis reveals the continued relevance of culture and language and suggests that there are limits to the concentration of high‐order service activity. This finding is corroborated by a more detailed look at occupational shifts within a variety of key economic sectors in Montreal and Toronto. We conclude by suggesting that these results and the model we put forward to explain them have implications that go beyond Canada: even in a globalizing world in which the constraints of distance are lessened, cultural and linguistic factors will continue to play an important role in determining the spatial distribution of high‐order economic activity.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".