Digital technology and Chinese-style industrial modernization: Dynamic threshold effect based on R&D Human resources
Bibliographic record
Abstract
Based on the construction of digital technology evaluation index system, this paper builds a dynamic threshold regression model to explore the complex impact of digital technology on Chinese-style industrial modernization under the threshold of R&D human resources, taking 30 provinces as research objects. It has been found that China's digital technology index is continuously improving, but there is a digital gap among regions. The beneficial impact of digital technology on Chinese-style industrial modernization has been thoroughly validated. Considering the threshold effect of R&D human resources, the influence of digital technology on Chinese-style industrial modernization exhibits nonlinear characteristics. With R&D human resources crossing the first threshold, it has shown a significant positive effect on Chinese-style industrial modernization, and the middle range of R&D human resources presents the optimal interval of the relationship between the two. The research findings offer a theoretical framework for advancing the construction of Chinese-style modernization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.001 |
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".