Planting the seed to grow local creative industries: The impacts of cultural districts and arts schools on economic development
Bibliographic record
Abstract
The impact of arts and culture on local economies has been studied extensively. However, a review of the literature finds conflicting and critical results regarding the impact of cultural on economic outcomes. In this paper, we shift attention to examine different intermediaries and concentrations of cultural agents that can influence growth and innovation in the “creative economy.” Thus, we build on previous work and expand on it by refining the scale of analysis (zip-code level). The paper focuses on education in the arts and digital media in all arts-related programs at universities as well as accredited art schools across the United States. Further, employing more observations for larger cities allows a richer depiction of the rather urban nature of the arts and digital media industries. We find that, by going to the zip-code level, we can say that both districts and arts programs (especially at schools that specialize in arts education) have a positive relationship with the share of jobs in the arts and digital media. Moreover, when we evaluate the impact of schools versus districts, we find that schools have a greater role.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".