Significance of Synergetic Strategy of Factors, Infrastructure Development, and Trade in Regional Integration—A Case Study of the Yangtze River Delta Region
Bibliographic record
Abstract
The Yangtze River Delta (YRD) is one of the regions with the most dynamic economy, the highest degree of openness, and the strongest innovation capability in China, possessing crucial factor markets, infrastructure development, and population resources. With the progress of China’s reform and opening-up policies and economic development, the YRD has achieved remarkable economic growth and social progress over the past few decades. Research on the economic and social integration of the YRD aims to deeply understand the development status of factors, infrastructure development, and trade within the region, and to explore how to address issues of inter-regional cooperation, coordination, and integration in the corresponding direction. Comprehensive research methods, including desk research, quantitative analysis, and qualitative analysis, are need to adopted in this research. This paper primarily proves through data collection that the research on regional factors, infrastructure development, and trade integration in the YRD is of great significance for promoting regional synergetic development, optimizing resource allocation, and enhancing overall competitiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".