Innovation within the Context of Local Economic Development and Planning: Perspectives of City Practitioners
Bibliographic record
Abstract
Although innovation is a major theme in current local economic development and planning, there is considerable uncertainty of what the concept specifically means, how it is measured, and how outcomes are identified. To date, no study has investigated this glaring gap in scholarship. To address this gap, we interviewed economic development practitioners across cities in Ontario to identify and clarify how they define, apply, and measure innovation within their cities’ economic development strategies. Practitioners indicate that innovation plays a key role in their cities’ economic development strategy, demonstrating the importance of the concept within local governments. Additionally, it is clear that local governments are key facilitators of innovation. While many cities claim to have some form of innovation in their economic development strategies, a wide range of framings and approaches to innovation exist. Cities may not be taking the most efficient approach to fostering local innovation, which is critical with the rise of knowledge-based economic development. Keywords cities; economic development; innovation; Ontario; policy; practitioners
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.025 | 0.039 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| 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".