Knowledge spillovers and the geography 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
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.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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