Creativity, clusters and the competitive advantage of cities
Bibliographic record
Abstract
Purpose – This paper aims to marry Michael Porter’s industrial cluster theory of traded and local clusters to Richard Florida’s occupational approach of creative and routine workers to gain a better understanding of the process of economic development. Design/methodology/approach – Combining these two approaches, four major industrial-occupational categories are identified. The shares of US employment in each – creative-in-traded, creative-in-local, routine-in-traded and routine-in-local – are calculated, and a correlation analysis is used to examine the relationship of each to regional economic development indicators. Findings – Economic growth and development is positively related to employment in the creative-in-traded category. While metros with a higher share of creative-in-traded employment enjoy higher wages and incomes overall, these benefits are not experienced by all worker categories. The share of creative-in-traded employment is also positively and significantly associated with higher inequality. After accounting for higher median housing costs, routine workers in both traded and local industries are found to be relatively worse off in metros with high shares of creative-in-traded employment, on average. Social implications – This work points to the imperative for the US Government and industry to upgrade routine jobs, which make up the majority of all employment, by increasing the creative content of this work. Originality/value – The research is among the first to systematically marry the industry and occupational approaches to clusters and economic development.
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 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 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".