Platform Placemaking Machines: Neighbourhood Place-Branding in Kingston Ontario
Bibliographic record
Abstract
Scholars have been writing about the marketing and branding of cities for decades. Place-branding, they argue, is not just about logos or advertisements but rather what those logos and advertisements say about the social, cultural, technological, and economic processes that shape and are shaped by a city’s character and reputation. In recent years, these processes shaping place-branding have radically changed, especially facilitated by the introduction of the “smart” phone and the ability of everyday actors to quickly generate just-in-time place-branding material known in marketing as “user-generated content makers”. This thesis will unpack these new user-generated content processes in the urban landscape and explain what these new processes mean for place-branding in the city. Research questions include: do these new actors and platforms in place branding create and/or influence new growth agendas for cities? What role do they play in directing resources to particular infrastructure over others? Whose voices are included, and who may be excluded? And lastly, is the future of place-branding becoming less about advertising and promotion and more a policy-making apparatus? Using Aaker’s (1997) Brand Personality Dimensions framework, I apply a mixed-method, case-study approach using a novel social sensing methodology to answer these questions in three neighbourhoods in Kingston, a small city in Ontario, Canada. This included 25 semistructured interviews with key urban actors in the city, a digital social sensing exercise with online platforms, archival and other non-digital media collection and coding. The findings call for a “nested neighbourhoods” approach to place branding, helping city brands absorb and be resilient to external shock factors. Neighbourhoods contribute to a more genuine place brand and are more likely to lead to citizens and residents as brand champions. A new model for place branding assessments, Place Branding Strength Assessment (PBSA) is presented for consideration. The findings recommend policy approaches to place branding instead of using slogans or traditional advertising approaches, again contributing to a more genuine place brand. The thesis explores limitations including drawbacks of using social media data as well as the challenges capturing alternative voices in a still dominate narrative of a pro-growth and pro-development city agenda.
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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 | 0.002 |
| 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 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".