What makes for a creative-friendly community? Untangling the location attributes of creative clusters
Bibliographic record
Abstract
Increasing investment in attracting creative clusters has become a quintessentially common practice at the municipal level, particularly in progressive cities, which have long been concerned with economic development. While rationales behind such investments are highly contingent on the potential economic outcomes of creative clusters, a thorough understanding of factors that help foster creative-friendly communities could facilitate municipal decision-making processes. This paper revisits factors relating to the location attributes of creative clusters that have hitherto remained insufficiently explored. Contributing to the development of creative-friendly communities, these factors complement the mainstream literature, which is overly preoccupied with the creative class theory, by addressing soft, cultural features as well as hard, material attributes, including the built environment, creative individuals, the local creative identity, networks and technology, leadership and the economic context, and the consumer market. This paper introduces a framework that incorporates those factors into three phases of creative activity, including idea-generation, production, and circulation/consumption, which operate on different tiers but nonetheless mutually interact. The paper reflects on the policy implications of the framework for local communities at large and concludes by proposing future avenues for research into how to promote creative-friendly communities.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".