EVALUATION OF THE COORDINATION BETWEEN CHINA’S TECHNOLOGY AND ECONOMY USING A GREY MULTIVARIATE COUPLING MODEL
Bibliographic record
Abstract
As extremely complex interactions exist in the process of economic research and development, a novel grey multivariable coupling model called CFGM(1,N) is proposed to evaluate the coordination degree between China’s technology and economy with limited information. This proposed model improves the aggregation in GM(1,N) model through the Choquet integral among λ-fuzzy measure, which can reflect interactions among factor indexes. Meanwhile, it can estimate the coordinate parameters via the whale optimization algorithm and obtains the coupling coordination degree combining with grey comentropy. To verify the proposed model, a case study using a dataset from China’s technology and the economic system is conducted. The CFGM(1,N) model has a better performance in the convergence and interpretability, as compared to the three heuristic algorithm and two classical approaches. Our finding suggests that China’s technology and the economic system is still relatively coordinated. Results also reveal that there exists strong negative cooperation between the comprehensive human input and the comprehensive capital investment in this system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".