Knowledge networks and dynamic capabilities as the new regional policy milieu. A social network analysis of the Campania biotechnology community in southern Italy
Bibliographic record
Abstract
A new definition of regional milieu is emerging from the recent innovation policy framework inspired by the notion of a ‘knowledge economy’. It is grounded in a theoretical context where the emphasis is on the interactive character of innovation, involving the sharing and exchange of different forms of knowledge among the actors. Identifying regional positioning within the global knowledge value chain is a current preoccupation of both policy and empirical research. This study tries to measure the degree of involvement of a (follower) regional community of biotechnology actors in the global knowledge value chain. It applies inductive research and exploratory case studies to analyse local relational behaviour within the knowledge network (KN) structure. Our description of a regional bio-community highlights the distinctiveness of regional knowledge in relation to the distribution of KN capabilities. The critical nodes in the KN structure are the intra-regional actors, represented by public basic research organizations. These actors bridge between local basic research groups and the international scientific community, although the ability of local actors to collaborate can affect the strength of the links among them. This aspect, which is not addressed by regional strategies, should be the focus of new regional policies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".