The Competitive and Comparative Advantages Effectively Fostered by National Innovation Systems
Bibliographic record
Abstract
The concept of National Innovation Systems (NIS) has extensively been applied to biotechnology and shaped the industry. This chapter aims to analyze and discuss the concept, its structure, configuration and prescriptive character, as well as the underlying competitive and comparative advantage assumptions. Its purpose is to provide a factual account of deployment efforts, and to highlight the challenges encountered with its implementation. To this end, an in-depth exploratory study of the berries sector in the Maule Region has been performed. Data were collected from NIS actors (academia, industry and government), inputs (funding R&D projects), outcomes of innovation activities (academic publications) and exports. The open source software VOSviewer version 1.5.4 was used to extract and analyze scientific publications on berries from Web of Science®. The relevance of links, interactions and implications are highlighted. Also, theoretical and prescriptive approaches to NIS implementation and deployment are bridged.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".