“It takes A Village”: An Examination of Intra-local Collaborative Economic Development Practices in Ontario, Canada, during the COVID-19 Pandemic
Bibliographic record
Abstract
Economic development practitioners have traditionally acted in isolation from their local counterparts, such as community organizations, businesses, and other municipal agencies. This type of economic development practice hinders practitioners’ ability to access available resources in their local economy and effectively undertake economic development. Local practitioners in Ontario, Canada, are no exception, as they typically engage in siloed economic development practices, characterized by a general lack of intra-local collaboration. The aim of this paper is to determine if the COVID-19 pandemic has facilitated local practitioners’ economic development practices in Ontario towards intra-local collaboration. To do so, thirty-seven in-depth interviews were conducted with senior local development practitioners in Ontario during the pandemic. The findings indicate that intra-local collaboration had been occurring in localities to a limited extent prior to the pandemic, but has since been intensified, despite several barriers. The gravitation towards intra-local collaboration was motivated by the tremendous challenges brought about by the pandemic, but underpinned by the realization that effective economic development cannot be undertaken in isolation, requiring collective engagement by local actors. During the pandemic, the practitioners intensified their intra-local collaborative practices to increase their access to available local resources, enhance their learning of best practices and acquisition of knowledge, and address common issues faced by various local actors.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".