The Uneven Economic Diversification of Small and Mid-Sized Canadian Cities, 1971-2016
Bibliographic record
Abstract
Economic diversification is a long-standing public policy goal in Canada, driven by concerns about resource dependence and the need to remain innovative and competitive in a complex global economy. While considerable economic diversification of Canada’s urban regions has been noted by a range of observers, the phenomenon remains only partially understood. We propose to study the economic diversification process using an entropy decomposition approach, with industrial composition data compiled from census responses between 1971 and 2016 for 125 small and mid-sized urban regions. We demonstrate that, while industrial concentration indeed declines for almost all regions studied during the study period, trends are highly variable between regions. In about half of regions, diversification was mainly driven by job loss in goods-producing industries rather than job growth in new activities, whereas among the other half of regions, diversification was weaker, but job growth was stronger. This suggests a need for caution in interpreting changes in industrial concentration indices as evidence of economic success stories.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".