Dynamics of the Canadian Manufacturing Sector in Metropolitan and Rural Regions
Bibliographic record
Abstract
This paper documents the changing geography of the Canadian manufacturing sector over a twenty-two year period (1976-1997). It does so by looking at the shifts in employment, as well as other measures of industrial change, across different levels of the rural/urban hierarchy - central cities, adjacent suburbs, medium and small cities, and rural areas. The analysis demonstrates that the most dramatic shifts in manufacturing employment were from the central cities of large metropolitan regions to their suburbs. Paralleling trends in the United States, rural regions of Canada have increased their share of manufacturing employment. Rising rural employment shares were due to declining employment shares of small cities and, to lesser degree, large urban regions. Increasing rural employment was particularly prominent in Quebec, where employment shifted away from the Montreal region. By way of contrast, Ontario's rural regions only maintained their share of employment and the Toronto region increased its share of provincial employment over the period. The changing fortunes of rural and urban areas was not the result of across-the-board shifts in manufacturing employment, but was the net outcome of differing locational patterns across industries. Change across the rural/urban hierarchy is also measured in terms of wage and productivity levels, diversity, and volatility. In contrast to the United States, wages and productivity in Canada do not consistently decline moving down the rural/urban hierarchy from the largest cities to the most rural parts of the country. Only after controlling for the types of manufacturing industries found in rural and urban regions is it apparent that wages and productivity decline with the size of place. The analysis also demonstrates that over time most rural and urban regions are diversifying across a wider variety of manufacturing industries and that shifts in employment shares across industries - a measure of economic instability - has for some rural/urban classifications increased modestly.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".