EFFECT OF GOVERNMENT SUBSIDIZATION ON CHINESE INDUSTRIAL FIRMS’ TECHNOLOGICAL INNOVATION EFFICIENCY: A STOCHASTIC FRONTIER ANALYSIS
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
This study aims to gain a better understanding of how effective government subsidization is in helping foster firms’ innovation. Drawing on the exploration/exploita- tion perspective and based on data collected from Statistical Yearbook on Science and Technology Activities of Industrial Enterprises, we look into the relationship between gov- ernment subsidization and Chinese firms’ innovation efficiency by applying a stochastic frontier analysis. The results show that when government subsidies are provided in small scale, firms’ innovation efficiency decreases; only when government subsidies increase to a certain scale, does firms’ innovation efficiency start to increase. We suggest that govern- ment subsidization would generate better innovation performance should it concentrate on a smaller number of firms at one time. As existing research is still inconclusive regarding the relationship between government subsidization and firms’ technological innovation output, we shed light on the issue by revealing a “U-shaped” relationship between the two.
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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 | 0.001 | 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