Competitiveness through cooperation: Analysis of spatial patterns and inter-jurisdictional collaboration in the place branding of Ontario communities, Canada
Bibliographic record
Abstract
Place branding is an important tool of economic development, underpinned by neoliberal policy and the processes of global competitiveness as communities at all geographic scales are becoming the primary nexus for economic development, and are forced to compete with one another for finite resources, direct investment, talent and immigrants, and tourism. Within the framework of global competition, it is often argued that communities can undertake inter-jurisdictional cooperation to enhance their competitiveness. To this end, this paper attempts to fill a gap in place branding literature by examining whether there are clusters of communities that currently have the potential to cooperate in their branding efforts. This research uses spatial autocorrelation of place brands amongst the communities of Ontario, Canada, to identify potential global patterns in branding and groups of neighbouring communities with similar brand messages. The results of this analysis show that Ontario has the potential for as many as 20 clusters in a variety of economic sectors. The existence of these clusters, therefore, provides a template for future place branding policy development within the province, allowing the participating communities an opportunity to remain relevant against global competitors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".