A Study on Chinese Regional Scientific Innovation Efficiency with a Perspective of Synergy Degree
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
From the perspective of the process of regional scientific innovation, the regional scientific innovation system is divided into two sub-systems of technology output and economic output. We utilize the chain-DEA method to assess scientific innovation and each sub-system’s efficiencies of 30 Chinese provinces from 2001 to 2011. Results suggest that the scientific innovation and each sub-system’s efficiencies need to be improved, unequilibrium in different regions exists evidently, each sub-system efficiency of one district varies a lot and the synergy degree remains low. Next, we compute the synergy degree of the two sub-systems using the synergy degree model. By employing linear regression model, an obvious positive correlation is demonstrated between the synergy and the scientific innovation efficiency. At last, based on the results of this study and real situations of Chinese scientific innovation, some suggestions are put forward accordingly.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it