Do Only Big Cities Innovate? Technological Maturity and the Location of Innovation
Bibliographic record
Abstract
Innovation enhances economic performance. High rates of innovation are associated with high rates of productivity growth, and faster productivity growth leads to higher real wages and improvements in standards of living. Consequently, many local policymakers are eager to encourage higher rates of innovation in their areas. Theoretical and empirical studies of the geography of innovation find that relatively populous regions are the most conducive to innovative activity. Large and densely populated places offer more developed markets for the specialized inputs used in innovation. Populous places also offer innovators greater opportunities to learn from one another. On the surface, these findings seem to offer little hope to smaller, more sparsely populated regions?places that would like to compete for innovative activity and the benefits of a knowledge economy. Are large populations a prerequisite for innovation? Orlando and Verba explore this common perception and find it is not always true. More populous regions dominate in relatively new technological fields, where innovations are more original. But less populous regions can compete in relatively mature technological fields, where innovations are more incremental. This finding should be of interest to research and development professionals?and to policymakers who are seeking ways to enhance regional innovative activity.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".