Developments of and Challenges to Basic Research Funding in Science and Engineering Sectors of Korea: The Role of National Research Foundation of Korea (NRF)
Bibliographic record
Abstract
Today, Korea faces a new challenge in the field of basic research: the country has attained a goal in terms of the amount of growth, but it needs to improve its quality of research. Korean researchers need to demonstrate excellence in their research, a far more critical issue than the number of papers published. Considering the current research ability in the country, they also need to show more creativity and forge better links with business.This paper calls for a new policy on research within basic science and technology, at a turning point in the history of research funding in Korea. In particular, it explains the development of research funding focusing on the NRF’s role, discusses the performance of and some problems in research funding, and finally makes recommendations for an NRF funding policy that could usher in a new era of research excellence in Korea. We hope this paper will help construct a theoretical framework for a basic research funding system for science and technology in Korea. In addition, we expect this paper will give some practical guidance for other developing countries trying to foster R&D capabilities in basic science.
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.
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 | 0.025 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".