Culture, Language, and the Location of High‐Order Service Functions: The Case of Montreal and Toronto
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it