TECHNICAL EFFICIENCY OF EFFORTS TO ENHANCE INNOVATIVENESS IN THE EUROPEAN UNION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it