Exploring the Path of High Performance Computing Accelerating New Quality Productivity to Economic Policy Innovation in the Perspective of Political Economy
Bibliographic record
Abstract
quality development. Taking the new quality productivity as the research perspective, this study selects the panel data of 30 provincial-level administrative regions in China from 2013 to 2023 as the research object, combines the regression analysis method to empirically analyze the influence paths between high-performance computing, the new quality productivity, and the economic policy innovation, and explores the role mechanism of the new quality productivity through the mediation effect analysis. The results show that the regression coefficients of high-performance computing on new quality productivity and economic policy innovation, as well as on new quality productivity and economic policy innovation, are all greater than 0 (p < 0.01), i.e., high-performance computing accelerates the formation of new quality productivity and further promotes the development of economic policy innovation. In the economically developed eastern region, this path of action is significant at the 1% level, and its driving effect of HPC accelerating new quality productivity on economic policy innovation is stronger, compared to the central, western and northeastern regions, which are significant at the 5% level. The article's analysis advances the understanding of the drivers of new quality productivity development and the effects, mechanisms, and regional differences of high-performance computing-enabled new quality productivity and economic policy innovation.
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How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".