Evaluation of Regional Innovation Networks: Based on Principal Component Analysis
Bibliographic record
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
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".