Evolving Business Centres in Canada: The Establishment versus The Next Wave
Bibliographic record
Abstract
Regional economic disparities are characteristic of Canada. Southern Ontario and Quebec have long been the home of many of the businesses that dominate the country’s economy, while business centres in the other regions of the country have played a lesser role in all but a few economic sectors. This paper studies the evolution of Canada’s system of business centres by contrasting the locational patterns of headquarters for two groups of businesses: the largest businesses in the country, the Canadian Establishment, and the fastest-growing businesses in the country, the Next Wave. The results show that while the country’s core region dominates both groups, the Next Wave is most highly attracted to suburban locations in the national core. The Canadian Establishment is dominated by central-city locations in Toronto and Montreal, as expected, but is also more spatially dispersed than the Next Wave at a provincial level of analysis. The paper provides a number of perspectives on these spatial distributions and suggests that the findings have meaning for the further development of business location theory. Key Words: location theory, quaternary location, corporate headquarters, economic development, establishment, next wave. Regional economic disparities are an enduringcharacteristic of Canada. In this sense, Canada is byno means unique, as countries worldwide have long grappled with the impacts of regional economic variations on the health and wellbeing of their cities and people. Italy’s industrialized and prosperous north contrasts with
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".