Growth and Location of Economic Activity: The Spatial Dynamics of Industries in Canada 1971–2001
Bibliographic record
Abstract
ABSTRACT A growing literature has accumulated that points to the stability of industrial location patterns. Can this be reconciled with spatial dynamics? This article starts with the premise that demonstrable regularities exist in the manner in which individual industries locate (and relocate) over space. For Canada, spatial distributions of employment are examined for seventy‐one industries over a thirty‐year period (1971–2001). Industry data is organized by “synthetic regions” based on urban size and distance criteria. “Typical” location patterns are identified for industry groupings. Industrial spatial concentrations are then compared over time using correlation analysis, showing a high degree of stability. Stable industrial location patterns are not, the article finds, incompatible with differential regional growth. Five spatial processes are identified, driving change. The chief driving force is the propensity of dynamic industries to start up in large metro areas, setting off a process of diffusion (for services) and crowding out (for manufacturing), offset by the centralizing impact of greater consumer mobility and falling transport costs. These changes do not, however, significantly alter therelativespatial distribution of most industries over time.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".