Digital transformation, productive services agglomeration and innovation performance
Bibliographic record
Abstract
Innovation is a necessary guarantee for sustainable development. Stepping into the digital age, digital transformation has triggered the innovation revolution. This paper takes 30 provinces in China from 2012 to 2022 as the research sample, we verify whether digital transformation has improved innovation performance. Based on the Solow growth model and agglomeration economics theory, we also explore the moderating role and threshold effect of agglomeration in productive service industry between digital transformation and innovation performance. To achieve this, we apply the methods of machine learning and text analysis to construct an evaluation index of regional digital transformation and measure it. The paper finds that China's digital transformation index is increasing, but there is a digital divide between regions. We also determine that digital transformation significantly and positively contributes to the level of innovation performance. Considering the threshold effect of agglomeration in productive service industry, the impact of digital transformation on innovation performance exhibits non-linear characteristics, As the level of agglomeration continues to exceed the threshold, the innovation-driven effect of digital transformation increases. The research results help clarify the relationship between digital transformation and innovation performance, and provide favorable policy directions for regional governments to identify digital divides and make reasonable industrial layouts. Thus, it can promote the construction of digital China and innovation power, injecting strong innovation force into the realization of SDGs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".