The changing geography of the Canadian manufacturing sector in metropolitan and rural regions, 1976–1997
Bibliographic record
Abstract
This paper documents the changing geography of the Canadian manufacturing sector over a 22‐year period (1976–1997). It does so by looking at the shifts in employment and differences in production worker wages 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 a lesser degree, large urban regions. Increasing rural employment was particularly prominent in Quebec, where employment shifted away from the Montreal region. The changing fortunes of rural and urban areas were not the result of across‐the‐board shifts in manufacturing employment, but were the net outcome of differing locational patterns across industries. In contrast to the situation in the United States, wages 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 decline with the size of place.
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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 | 0.005 | 0.013 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".