R&D 투자의 총요소생산성 효과에 대한 국제비교 : 우리나라와 OECD 및 주요국가를 중심으로
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.014 |
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