Can creative cities be inclusive too? How do Dubai, Amsterdam and Toronto navigate the tensions between creativity and inclusiveness in their adoption of city brands and policy initiatives?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Creative cities tend to generate higher levels of innovation and economic growth as well as be vibrant places to live. Many cities in the world have adopted the creative city label to realise these benefits. It is not certain, and in fact disputed by authors such as Richard Florida (2017), that creative cities will also show high levels of inclusion. Inclusiveness is a multi-dimensional concept that needs to be unbundled before its connection with creativity is firmly established. Various tensions can arise when cities decide to adopt both creative city and inclusive city branding and urban policy initiatives. This paper studies these tensions in formulating responses to two main questions: A) How can the concepts ‘creative city’ and ‘inclusive city’ be operationalised, measured, and related to each other? and B) How do cities that adopt these two city labels implement them in their city branding and policy initiatives? What can we say about the internal consistency of these brands and policies? We have chosen Dubai, Amsterdam, and Toronto as case studies since all three enjoy good reputations in both creativity and inclusion in their respective continents and contexts. Our study indicates that cities promise more than they deliver, that creativity matches some aspects of inclusion, but contradicts others. Moreover, in case of tension, creativity always prevails over inclusion, whereby economic interests come first, and only aspects of inclusion that add to or are at least not in conflict with creativity tend to be honoured. Finally, in each of the three cities, the ‘couleur locale’ can clearly be observed in terms of the aspects of inclusion that are emphasised, and which tend to be disregarded.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it