Manufacturing change and policy response in the contemporary economic landscape: how cities in Ontario, Canada, understand and plan for manufacturing
Bibliographic record
Abstract
Despite its nearly half-century decline in Western countries, manufacturing remains a vital part of local and regional economies. This importance is reflected in the economic development policies of cities, which are struggling to understand the current state of manufacturing and what they should do to either reinforce or replace it. Within the scholarship and practice, however, there is limited understanding of the policies that are adopted and whether they have potential to have a meaningful impact for the cities that adopt them. To address this gap, this research considers the way that manufacturing is contextualized and responded to within the local economic development planning documents of 47 (of 51) cities in Ontario, Canada. Through a comprehensive content analysis, it examines whether there are variations in the way cities approach manufacturing. Based on chi-square analysis, the findings show that there is considerable homogeneity in the way that cities of all sizes are approaching manufacturing, suggesting they are not adequately considering the local context in their policy, and rather focus on more general and previously adopted approaches. However, there is an emerging spatiality to the policy that was identified, which presents cities with a pathway forward to address manufacturing within their local economic development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.025 | 0.013 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".