Do Only Big Cities Innovate? Technological Maturity and the Location of Innovation
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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 it