The Growing Economic Specialization of Cities: Disentangling Industrial and Functional Dimensions in the <scp>C</scp>anadian Urban System, 1971–2006
Bibliographic record
Abstract
Abstract Decreasing spatial transaction and trade costs have given rise to growing economic specialization of cities. While most studies focus on industries as the primary manifestation of urban specialization, a growing body of literature examines occupational functions, i.e., activities and tasks performed within a given industry or firm. This paper explores how the two dimensions (industries and functions) interact across the urban system and their relative importance over time. Is there a trend toward increasing functional specialization in the Canadian urban system? How much of this phenomenon is attributable to spatial shifts in regional industrial structures as opposed to spatial divisions within industries? The paper uses a unique data set drawn from Statistics Canada Census microdata files between 1971 and 2006. Based on the employed population, the data are spatially organized and cross‐tabulated over industries and occupational groups. A decomposition methodology is used to compare the relative weights of industry and regional (functional) effects in accounting for the changing spatial division of functions across Canadian urban areas. Clear patterns of increasing functional specialization are found within the Canadian urban system. Regional effects are generally greater than industry effects, suggesting that spatial divisions of functions (spatial shifts within industries) are progressing more rapidly than regional shifts in industrial structure.
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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 | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".