Bibliographic record
Abstract
In this paper, we conduct a comparative study for the effect of R&D investment on total factor productivity(TFP) in order to compare the efficiency of R&D spending among different OECD countries. For the analysis, we first pool the data from 22 OECD member countries during 1981-2004, and estimate TFP elasticity of R&D investment of that group. Then we evaluate TFP elasticity of R&D investment of five major advanced countries such as USA, Japan, Canada, Italy and Korea from 1970 to 2004. Finally, we investigate whether the efficiency of R&D investment of Korea has improved over the period by dividing the period into two parts, before and after 1990. From the empirical analysis, we find that the estimated R&D efficiency of Korea has been similar to that of a group of 22 OECD countries. The estimated R&D efficiency of Korea has been higher than those of Canada and Italy, but lower than those of USA and Japan. In sum, the R&D efficiency of Korea has reached the average level of OECD member countries. In addition, we find that the R&D efficiency of Korea after 1990 has been much higher than the efficiency before 1990. Thus, we can say that the R&D efficiency of Korea has substantially improved over the course of a period. We surmise that increased R&D spending done by major conglomerates and technological innovation have contributed the improvement of R&D efficiency since the year of 1990.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".