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Record W2993262921 · doi:10.36939/cjur/vol26no2/art94

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

2019· article· en· W2993262921 on OpenAlexafffundvenueabout
Evan Cleave, Ben Watson McCauley, Godwin Arku

Bibliographic record

VenueCanadian journal of urban research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsQueen's UniversityWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPlace brandingPublic relationsKey (lock)Process (computing)Best practicePolitical scienceBusinessSociologyComputer scienceTourismLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.317
GPT teacher head0.393
Teacher spread0.076 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2019
Admission routes4
Has abstractyes

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