Evaluation of Regional Innovation Networks: Based on Principal Component Analysis/EVALUATIONS SUR LES RÉSEAUX RÉGIONAUX D'INNOVATION: BASÉES SUR L'ANALYSE DES COMPOSANTES PRINCIPALES
Bibliographic record
Abstract
Abstract: Regional Innovation Networks is becoming a main pattern in regional innovation and development. To figure out the characteristics of regional innovation networks, this paper is based on the sociology theory of networks relationship and structure analysis. It evaluates the whole situation of regional innovation networks of China and concludes that the degree of opening, the communication strength among the main innovation individuals within the region, the scale of the regional node, and so on, such kind of factors have great influences with the regional innovation networks. Above that, this paper also analyzes the reasons of existing problems and puts forward counterpart suggestions. Keywords: regional innovation networks; relationship; structure; principle component analysis Resume: Les reseaux regionaux d'innovation deviennent un motif principal du developpment de l'innovation et de l'economie regionales. Pour determiner les proprietes structurales et relationnelles des reseaux regionaux d'innovation, cet article analyse les caracteristiques des reseaux regionaux d'innovation en utilisant les theories sociologiques sur les relations et les structures de reseaux. Il evalue le niveau global des reseaux regionaux d'innovation en utilisant les methodes d'analyse des composantes principales et en conclu que le degre d'ouverture, l'intensite d'echanges entre les sujets d'innovation dans les reseaux regionaux et l'ampleur de noeud regional ont une influence importante sur les reseaux regionaux d'innovation. Sur cette base, l'article analyse egalement les problemes existants et en donne des propositions. Mots-cles: reseaux regionaux d'innovation; relations; structures; analyse des composantes principales INTRODUCTION Since J.A. Schumpeter put forward the concept of innovation, it has become one of the most focused topics in economics research. The research of innovation theory follows the pattern through Linear Mode to Non-linear Mode, and has become the theoretical and practical foundation of innovation networks theory. Freeman C. published an article in Research Policy in 1991, and clearly mentioned the concept of innovation networks for the first time. Freeman uses the terms of Networks of Innovators, Innovation networks, and Networks of Innovation as the same meaning at the same time, and takes the innovation networks as one of a basic institutional arrangement. He concludes that the structures of the networks are mainly contributed by innovative cooperation among the enterprises and are aimed to improve the capability to increase sales and revenues (Freeman C, 1991). Since then, more and more scholars began to concern about innovation networks. There are two main aspects to dig into innovation networks, one of which is the theory of innovation, and the other of which is the research on industry clusters. They are two counterpart levels of the innovation networks research, which are enterprises innovation networks and regional innovation networks (LI Jin-hua, 2009). Up to now, scholars in or abroad have defined the concept of innovation from national innovation systems, innovation factors synergies, resources co-complements, industry clusters, and so on aspects, and have figured out some of the structures and characteristics of regional innovation networks. It has been a main stream method to research regional innovation networks with social network analysis theory. Mitchell concludes in 1969 that people should take count of the scales, strictures, the interactive relationships and the process, and other factors to do network characteristic analysis. Granovetter defines one of the important characteristics, Strength, of the relationships in the networks in his famous paper The Strength of Weak Ties for the first time in 1973, and has made the beginning to concern about Relationships in network analysis. After that, he brings in the concept of Embedded and defines between Relational Embedding and Structural Embedding in 1985. …
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".