Research on the Coupling of Human Resource Structure and Industrial Structure: A Survey from Nine Provinces of the Yellow River Basin in China
Bibliographic record
Abstract
To realize the benign coupling between human resource structure and industrial structure, and promote high-quality economic development, the article analyzes the coupling relationship between human resource and industrial structure, conducts a comparative study on the coupling between human resource and industrial structure in nine provinces in the Yellow River Basin by using 2020 statistics. The results showed: the coupling degree of primary industry in each area is far less than 1, indicating a surplus of human resources. The coupling degree of the secondary industry is greater than 1, showing a shortage of human resources varies in the upper and lower reaches of the river. While the lake of human resources in the tertiary industry goes in the middle and upper reaches of the river, except for Ningxia Province, where there is a surplus of human resources. In this regard, the government needs strengthen the top-level design, promote the integration of human resources, and realize the positive coupling between the human resources structure and industrial structure in the Yellow River Basin.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".