Bibliographic record
Abstract
Rapid economic development compels the inter-countries, inter-regions and inter-enterprises competition mode changes. Nowadays, the inter-regions competition has actually become the competition of innovations between regions. The economic benefits of the regional innovation has become an important driving force for the regional development. Social capital has become a catalyst for improving regional innovation capacity as it can improve the efficiency of social networks, trust and norms through the promotion of cooperative behavior. Based on the cross-section data from 31 provinces of China's mainland,this paper explored and measured the regional innovation capacity from the perspectives of knowledge creation,knowledge acquisition, enterprise innovation, innovation environment and innovation performance by using blood donation rate,trust and density of social organizations as indexes. The measuring system and index weights of the regional innovation capacity in this study were sourced from China's Regional Innovation Capacity Report 2010. The study also analyzed the relationship between the social capital and the regional innovation capacity through correlation analysis and multiple regression model. The results showed that the innovation capacity of the eastern region of China was far ahead of the northeastern, the central and the western regions,particularly in enterprise innovation capacity and innovation performances. The innovation capacity of Tibet was much higher than the capacity of the central and the western regions due to the religions and the blood donation rate contributed by the army. The social capital and the innovation capacity significantly varied among provinces but presented a similarity in the spatial distributions. The regional innovation capacity had a significant and positive correlation with the trust but negative correlations with the standards and the network. The regional innovation capacity also had positive correlations with the GDP per capita, research and development personnel and the number of the research institutes in the region. At the end this paper proposed policies to improve the social capital and enhance the regional innovation capacity.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".