The analysis of allocation efficiency of R&D resource and its determinants — An empirical study of panel data from 31 countries
Bibliographic record
Abstract
The paper employs Stochastic Frontier Approach to analyze the allocation efficiency of R&D resources in 31 countries by Frontier 4.1 software. It reveals that U.S.A, Japan, Germany and Britain are the big power of R&D input and output, yet their R&D efficiency is not so high; Switzland, Sweden, South Korea, Canada and Australian have the highest R&D efficiency, and the R&D efficiency of China, India, Brazil and South Africa are relatively high. With the global R&D activities transferring to the region of lower cost and high efficiency, R&D Emerging Economies are arising. According to output elasticity of R&D resources, human labor is much stronger than that of funds. Furthermore, the output elasticity of human labor in high-income countries is stronger than that of middle-income countries, but the output elasticity of funds in high-income countries is weaker than that of middle-income countries.
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How this classification was reachedexpand
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".