Measurement of the Innovation Efficiency of the Hi-tech Industry in China and Its Influencing Factors
Bibliographic record
Abstract
Accelerating the development of the hi-tech industry is a key measure to effectively implement the "Made in China 2025" strategy.In order to measure the innovation efficiency of the hitech industry, this paper establishes an evaluation index system for the innovation input and output of the hi-tech industry.Then, based on the data of the input and output variables collected, this paper uses the CCR model to measure the innovation efficiency of the hi-tech industry in 30 provinces and municipalities of China during the period of 2002-2016.At the same time, it investigates the spatial correlation of the innovation efficiency of the hi-tech industry, and empirically analyzes the influencing factors to the innovation efficiency of the hi-tech industry using a spatial measurement model.According to the results of this study, the innovation efficiency of the hi-tech industry exhibit significant provincial differences.The provinces and municipalities where the hi-tech industry is of high innovation efficiency are mainly located in the eastern coastal areas, while the innovation efficiency of the hi-tech industry in those most mid-western inland provinces and municipalities is less than satisfactory.In this paper, China is divided into three major regions -East China, Central China and West China.It is found that the innovation efficiency of the hi-tech industry in these three regions showed basically the same trend during the sample period, but with serious regional differentiation -the innovation efficiency of the hi-tech industry was the highest in the east, the second highest in the central region, and the lowest in the west.The global Moran's I index was all positive during the sample period and passed the significance level test, indicating that the innovation efficiency of the hi-tech industry in China has significant spatial correlation.The spatial LISA chart shows that the innovation efficiency of the hi-tech industry in most provinces and municipalities is located in the spatially clustered quadrants, while that in only a few provinces and municipalities is located in the spatially scattered quadrants.The results of the spatial measurement model shows that the R&D intensity, opening-up and human capital have positive effects in promoting the innovation efficiency of the hi-tech industry, that government intervention clearly hinders the improvement of the innovation efficiency and that enterprise size and industrial structure exhibit no significant effects.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".