Entrepreneurial Space and Enterprise Richness In a Group of U.S. Counties Before, During, and After Economic Turmoil
Bibliographic record
Abstract
The importance of economic diversity as a business measure prompted an investigation of the association between a period of economic turmoil (Great Recession) and the power law relationships of enterprise richness—a business diversity measure—and enterprise numbers—an expression of total entrepreneurship—in 22 U.S. counties. Before the onset of the turmoil from 2000–2007, the total enterprise numbers in the counties increased steadily. With the onset between 2008 and 2011, they declined sharply and thereafter from 2012–2016 continued decreasing slowly. However, enterprise richness–enterprise numbers relationships—expressed as power laws—were fairly stable before, during and after the turmoil. These power laws are apparently robust and not temporally sensitive. The power laws potentially provide predictive powers about different entrepreneurial typesin U.S. counties: that is, new, existing, and total entrepreneurship. Keywords: U.S. counties, entrepreneurial space, enterprise dynamics, enterprise richness, economic turmoil, Great Recession
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".