The importance of local characteristics: An examination of Canadian cities' resilience during the 2020 economic crisis
Bibliographic record
Abstract
This paper examines the resilience of Canadian cities to the 2020 economic crisis that followed the 2019 coronavirus pandemic. It specifically investigates the resistance and recoverability (i.e., very short‐term recovery) dimensions of resilience. It reveals that Canadian cities exhibited heterogeneous resiliency to the crisis, resulting in economic restructuring. Further, the paper decomposed resilience into an industrial mix effect and local‐specific effect using a shift‐share analysis, to move away from the industrial‐structure dominated focus in the resilience literature by examining the influence of local‐specific effects. The analysis found that local‐specific effects played a dominant role in determining the resiliency of cities, while their industrial mix had a marginal influence. Moreover, the determinants of resilience are complex having different effects and functions depending on the dimension of resilience under examination. Also, the determinants of resilience may change depending on the type of shock cities experience. A key policy implication is that localities' capabilities largely determine their resilience.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".