Measurement of National Innovation Driving Force and Its Promotion of High-Quality Development of Service Industry
Bibliographic record
Abstract
This paper mainly verifies whether the national innovation driving force (NIDF) can effectively promote the high-quality development (HQD) of the service industry. Specifically, the authors calculated the NIDF index, and tested the influence of NIDF and its internal indices on the domestic value-added ratio (DVAR) of export in the service industry. Besides, the innovation intensity of the service industry was measured to analyzed the heterogeneity of the industry. The empirical results show that: Stronger NIDF can significantly elevate the export DVAR of the service industry, promoting the HQD of that industry. Among the dimensions of NIDF, institutional innovation has relatively great positive impact on the HQD of the service industry. Moreover, the influence of NIDF on a sector of the service industry varies with the innovation intensity of the sector. The research results provide new evidence for the promoting effect of innovation on the HQD of the service industry.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".