Practitioners' ability to retool the economy: The role of agency in local economic resilience to plant closures in Ontario
Bibliographic record
Abstract
Abstract Economic resilience focuses on how localities react and respond to shocks. A recent conceptual advancement, known as the agency perspective and rooted in evolutionary thinking, highlights that economic actors (e.g., practitioners, firms, and institutions) play an essential role in localities' resilience. However, empirical investigations into the role of economic actors have been scarce. To address this shortcoming, in this study we conducted in‐depth interviews with 22 practitioners (economic development officials) from various cities in Ontario, Canada. Through an evolutionary lens, we examine practitioners' perceptions of and responses to endogenous shocks—notably, major industrial plant closures over the past 20 years. We find that practitioners influence localities' resilience through each dimension of the resilience process. Most notably, they support their localities' adaption through various short‐ and long‐term adaptive strategies. Also, we find that economic actors have different capacities and resources at their disposal to respond to shocks depending on the size of their city and its geographical location. Regarding the latter, we reiterate a commonly noted north‐south divide in the province. In addition, we find that economic actors are constrained by multi‐scalar policies, and thus operate in the confines of existing power and political structures.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".