Does Government Purchasing Science and Technology Public Service Promote Regional S&T Innovation Ability? Evidence from China
Bibliographic record
Abstract
During the development of scientific and technological innovation, the importance of Government Purchasing Public Services (GPPS) in the field of science and technology (S&T) has become increasingly prominent. To investigate the relationship between Government Purchasing Science and Technology Public Services (GPSTPS) and regional S&T innovation ability, this paper first constructs a PMC index model to estimate GPSTPS objectively. Then, the spatial econometric model is adopted to explore the impact of GPSTPS policy on the regional S&T innovation ability based on the provincial panel data from 2008 to 2017 in China. Results show that: (1) Regional S&T innovation ability has a significant spatial positive correlation in geographical space from 2008 to 2017. (2) From the overall perspective, the GPSTPS policy does not play a role in improving the regional S&T innovation ability. (3) From the perspective of subregions, there are differences in the impact of GPSTPS on the regional S&T innovation ability between the eastern, central and western regions of China. (4) From the perspective of spatial spillover effect, the policy of GPSTPS has a positive spatial spillover effect on the improvement of regional S&T innovation ability in the eastern region, while the effect is not obvious in central and western regions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".