Relative geographic concentration of creative, other traded, and local industries using establishment data and Harvard's U.S. Cluster Mapping Benchmark Definitions
Bibliographic record
Abstract
This paper examines the relative tendency of industries and industry clusters to be geographically concentrated. Creative industries defined as having distinct artistic creation and production-distribution components are examined. This extends previous observations that creative industries exhibit a relatively high degree of geographic concentration to examine whether two-sided market dynamics contribute to this concentration. Variance in the distribution of business establishments among U.S. metro areas for 978 industries is calculated using County Business Patterns data from the U.S. Census Bureau. The data is mapped to different clusters using Harvard University's U.S. Cluster Mapping Benchmark Definitions. The average variance of each cluster is calculated to measure relative concentration.•Richard Caves' definition of creative industries is used to identify industries characterized by a two-sided structure.•Harvard University's U.S. Cluster Mapping Benchmark Definitions are used to map creative industries to specific industry codes and industry clusters.•These two methods are applied to U.S. County Business Patterns data to examine the relative geographic concentration of two-sided creative clusters.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".