Urban Start-up Districts: Mapping Venture Capital and Start-up Activity Across ZIP Codes
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
Previous research has identified the clustering of high-tech industries, entrepreneurial start-ups, and venture capital across metropolitan areas. Using detailed ZIP code data on start-up activity and venture capital investment, this research tests three hypotheses informed by urban theory on the clustering of innovation, entrepreneurship, and high-technology industry: (1) that start-up activity and venture capital investment will concentrate in distinct microclusters within metro areas, (2) that a substantial level of start-up activity and venture capital investment will cluster in dense urban neighborhoods or ZIP codes, and (3) that the clustering of start-ups and venture capital investment will vary by industry or type of technology. The authors find evidence to support all three. Start-up activity and venture capital investment are concentrated in a relatively small number of ZIP codes in the United States, the majority of which are in dense urban neighborhoods, and this clustering varies by industry and type of technology.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it