Technological Profiles and Technology Trade Flows for Some European and OECD Countries
Bibliographic record
Abstract
In the current context of increasing globalisation, innovation and investment in R&D become crucial. Furthermore, European Lisbon strategy gave disappointing results in terms of R&D expenditures and patents. In this context, our paper deals with some OECD characteristics concerning their connection to global markets of knowledge and technology trade. Statistical results show that the USA are great performers in terms of R&D expenditures and patents, with large openness to foreign collaboration, Japan is successful in innovative activities while quite isolated from global research network. The European Union seems to be in the opposite situation. Nevertheless contrasted situations are observed, depending on the European countries. For instance Sweden, Finland and Denmark register quite good results in terms of R&D. Concerning technological trade, Technology Balance of Payments (TBP) statistics give some additional results. While the EU15 used to exhibiting a TBP deficit, the situation has changed since 2006 and the EU15 registers a surplus. This performance relies on Germany, Sweden and Austria, which are the main exporters of technology among European countries. Thanks to this first statistical analysis, it seems that technological profiles of OECD countries impact on technological trade flows. Except some specific countries’ characteristics, leaders in R&D are quite active in terms of technological exports and also imports. For instance, the European leaders in R&D export their technologies but seem also active by importing technologies from abroad.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".