Just Because You Could, Doesn’t Mean You Should: Exploring if (and When) Cities Should Brand Through a Case Study of The City of London, Ontario
Bibliographic record
Abstract
Cities in Canada and abroad are engaging in place branding initiatives without any true understanding of whether they are likely to succeed. A key reason for this uncertainty is that there is a lack of understanding of what local conditions are needed to ensure the best chance for success. This study addresses this uncertainty in two ways: first, a theoretical framework is developed to identify local characteristics and conditions that are requisite for place branding; and second, the City of London, Ontario is used as a case study to examine whether small and midsized cities should be branding. Based on an extensive review of the literature domain a framework of seven criteria was developed: is there a need? Is there something to be branded? Is there local capacity and knowledge? Is it part of strategic planning? Is there leadership? Is there coordination? And is the process inclusive? Based on interviews with sixteen key stakeholders in London (both local officials and community stakeholders), it is clear that the city meets very few of these criteria. This suggests that London – and likely most other small and midsized cities in Canada and abroad need to be measured in their approaches to place branding.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".