The Assessment of the Role of University Within National Knowledge-Based Innovation System Based on the Comparative Study of Selected Innovative Countries and China
Bibliographic record
Abstract
University is an important part and one of the behavior bodies of national knowledge-based innovation system. How to understand the role of university played in knowledge-based innovation system and make it be used to its fullest potential is of great significance to promote technological progress and transition, to construct the national knowledge-based innovation system with Chinese characteristic within which higher education and scientific research are deeply integrated. Universities of 7 innovative countries and China are investigated, 9 indexes which reflect the input and output of knowledge-based innovation of university were given weight and calculated by using entropy method, so that the difference of the roles universities from different countries played can be measured. Results from comparing universities of selected innovative countries and China shows:(1) The knowledge innovation capability of American university is strongest among the selected countries and the role of American university is most prominent in its knowledge-based innovation system.(2) Chinese and Korean universities' input and output of knowledge innovation are weakest but the input-output efficiency of universities in these two countries are both higher than that of universities in Canada and German.(3) Although the importance of Chinese university in knowledge-based innovation system has not completely showed yet, the gap between the role of Chinese university and other selected innovative countries' universities played in knowledge-based innovation system are gradually narrowing.(4) University's RD input, the number of Nobel Prize wined and the amount of postgraduates are crucial factors that exert great effect on the knowledge innovation capability of university and its position in knowledge-based innovation system. Therefore, this essay suggests China uses the experiences of innovative countries as references, optimizes the function of university within knowledge-based innovation system and thus builds Chinese national knowledge-based innovation system within which higher education is organically combined with scientific research.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".