Economic growth and restructuring in Canada's heartland and hinterland: From shift‐share to multifactor partitioning
Bibliographic record
Abstract
The geography of the Canadian economy has long been dominated by heartland‐hinterland contrasts, with manufacturing identified as the dominant function of most heartland cities in analyses of the 1961 and 1971 census data. However, the proportion of employment in manufacturing has been declining in the heartland provinces of Ontario and Quebec over the past fifty years and some geographers argue that the heartland‐hinterland dimension of the regional economy is being overridden by city‐regions that are integrated into global networks of production and trade. The heartland‐hinterland trends are examined using multifactor partitioning (MFP), an advanced shift‐share methodology, for the period of 2001–2006. This is the first intercensal period in which Canadian business has faced the full impact of the removal of North American tariff protection and the increased globalization of the Canadian economy. The data covers employment by eighteen industry sectors for the seventy‐three economic regions defined by Statistics Canada. MFP measures the region and industry‐mix effects, which are interpreted as in the traditional shift‐share model (though they are derived more accurately) and, in addition, an interaction effect. The results demonstrate that the broad heartland‐hinterland differences in the distribution of population and employment growth are increasing not decreasing and that the hinterland is in fact falling further behind the heartland in employment growth. However the Calgary‐Edmonton corridor and the Lower Mainland of British Columbia are emerging as a western heartland. The population size of cities does affect their rates of employment growth, but so too does their location: the growth of heartland cities is outpacing those in the hinterland. The Appendix provides the equations for two‐variable multifactor partitioning.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".