Beyond territory : dynamic geographies of knowledge creation, diffusion, and innovation
Bibliographic record
Abstract
Introduction 1. Territorial and Relational Dynamics in Knowledge Creation Diffusion and Innovation: An Introduction Harald Bathelt, Maryann P. Feldman and Dieter F. Kogler Part 1: Agglomeration - Aspects of Specialization and Diversity 2. Where do They Come From, and to Whom do they Flow? W. Mark Brown and David L. Rigby 3. Local Diversity and Creative Economic Activity in Canadian City-Regions Greg Spencer 4. Technological Relatedness and Regional Branching Ron Boschma and Koen Frenken 5. Evolution of the Geographical Concentration Pattern of the Danish IT Sector Christian R. Ostergaard and Bent Dalum Part 2: Beyond Territory - Evoutionary Spatio-Sectoral Dynamics 6. The Emerging Industry Puzzle: Optics Unplugged Maryann P. Feldman and Iryna Lendel 7. Food Geography and the Organic Empire Modern Quests for Cultural-Creative Related Theory Phil Cooke 8. Beyond Spillovers - Interrogating Innovation and Creativity in the Peripheries Andrey N. Petrov 9. The BioValley - Knowledge Dyanmics in a TNC headquarter location Bernhard Fuhrer and Paul Messerli Part 3: Making Connections - Bridging the Local and the Global 10. Islands of Expertise - Global Knowledge Transfer in a Technology Service Firm Johannes Gluckler 11. The Changing and Diverse Roles of RIS in the Globalizing Knowledge Economy: A Theoretical Re-Examination with Illustrations from the Nordic Countries Bjorn Asheim, Arne Isaksen, Jerker Moodysson, Markku Sotarauta 12. Globaal Buzz at International Trade Fairs: A Relational Perspective Nina Schuldt and Harald Bathelt 13. Dyanic Geographies of Knowledge Creation, Diffusion and Innovation: Present and Future Diections Dieter F. Kogler, Harald Bathelt and Maryann P. Feldman
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".