Cities’ economic development efforts in a changing global economy: content analysis of economic development plans in Ontario, Canada
Bibliographic record
Abstract
Over the past few years, cities have been increasingly adopting a written plan to guide economic development processes. This is in sharp contrast to past practices which were haphazard and unsystematic. So far, there has been no comprehensive overview of the policies, strategies and focus of these written documents. Focusing on the Province of Ontario, Canada, we undertook a systematic content analysis of the most recent documents for each city. Specific codes were developed for the analysis of the documents. While the majority of cities in Ontario were identified as having codified a formal economic development plan (a plan was identified in 41 of 51 cities), there was considerable variability in how the plans were developed (such as through the use of private consultants) and presented, with noticeable differences in the information given about the community, complexity of analysis conducted and the details on the economic development policies that are being pursued. Despite the range of document formats, there was notable uniformity in the content of the policy directives that were presented. In terms of economic development focus, traditional manufacturing is essentially ignored in favour of attracting advanced manufacturing and knowledge‐based industries. Additionally, due to the extensive reliance on private external consultants, the plans have a homogenous nature, where contemporary ideas such as diversification, place branding and marketing, downtown redevelopment, focus on creative and knowledge industries, and tourism are constantly regurgitated. Conspicuously missing among the strategies were regional collaborations and cluster development. Although the adoption of a written plan represents an important milestone in local economic development policymaking, a number of key limitations were identified within the current documents, and the paper offers direction for a more effective future policy development.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".