Globalization of innovation networks: A model of the process
Bibliographic record
Abstract
Research on innovation networks has increasingly considered their internationalization as a natural later stage in their evolution, necessitated by augmented competition and acceleration of technology cycles. A complex dynamic ensues in such later stages as firms and other actors in the innovation network begin to pursue their own interest in the innovation space, as it grows from local to global contexts. Grounded in complex system theory, in this paper we present a theoretical model of the interaction between centrifugal and centripetal forces that shape the decision making space in which entrepreneurs and higher management act as interdependent actors in a complex system. We base this model on an analysis of the way in which locally available resources, such as talent and knowledge interact with evolving business models, as technology-based firms face economic and technological uncertainties, and we set forth testable propositions derived from the model with the aim of identifying likely evolutionary paths in other innovation networks, and. Interview data from firms in the Vancouver fuel cell cluster is used to illustrate different components and processes in the model. Policy implications for innovation at the regional level are discussed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".