Knowledge spillovers and the geography of innovation
Bibliographic record
Abstract
This chapter focuses on the geographic dimensions of knowledge spillovers. The starting point comes from the economics of innovation and technological change. This tradition focused on the innovation production function however it was aspatial or insensitive to issues involving location and geography. However, empirical results hinted that knowledge production had a spatial dimension. Armed with a new theoretical understanding about the role and significance of knowledge spillovers and the manner in which they are localized, scholars began to estimate the knowledge production function with a spatial dimension. Location and geographic space have become key factors in explaining the determinants of innovation and technological change. The chapter also identifies new insights that have sought to penetrate the black box of geographic space by addressing a limitation inherent in the model of the knowledge production. These insights come from a rich tradition of analyzing the role of both localization and urbanization economies, by extending the focus to the organization of economic activity within a spatial dimension and examine how different organizational aspects influence economic performance. While the endogenous growth theory emphasizes the importance of investments in research and development and human capital, a research agenda needs to be mapped out identifying the role that investments in spillover conduits can make in generating economic growth. It may be that a mapping of the process by which new knowledge is created, externalized and commercialized, hold the key to providing the microeconomic linkages to endogenous macroeconomic growth.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".