TECHNICAL EFFICIENCY OF EFFORTS TO ENHANCE INNOVATIVENESS IN THE EUROPEAN UNION
Bibliographic record
Abstract
The objective of this paper is to clarify whether or not the so-called innovation leaders are efficient in transforming innovation inputs into outputs. The study aims to expand on the thought that the level of inputs is decisive in classification of countries as leaders, followers, or laggards in the race to improve innovativeness and competitiveness, and thus raise the standard of living. Based upon the European Innovation Scoreboard (EIS), the efficiency of investment in innovation is examined with the use of the DEA model. The use of the EIS as the main source imposes a limitation on the scope of the countries examined, yet the EIS is essentially the only comprehensive source that examines innovativeness. It is observed that the so-called laggards in innovation are often efficient in their use of resources, whereas leaders of innovation fall short in the area of returns to scale and congestion. Such an observation provides an important guide to the development of policies aimed at improving innovative efforts. Finally, through the use of the nonparametric DEA model, this paper provides a methodological extension to the methods for investigation of innovation systems.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".