Industrial Transfer and Spatial Structure Optimization of Beijing, Tianjin and Hebei Province
Bibliographic record
Abstract
In order to fully promote the industrial coordination development in Beijing, Tianjin and Hebei Province and form a development structure with rational spatial patterns and optimized allocation of various industries, this study uses the market potential model based on the new economic geography to, quantitatively analyse the market potential and spatial pattern of Beijing, Tianjin and various cities in Hebei Province from 2012 to 2019, obtains the dominant industries of Beijing, Tianjin and Hebei Province.Then, the study adopts the industrial location quotient model to perform empirical study on the industrial conditions of Beijing, Tianjin and Hebei Province and explore the location and routing problem of industrial transfer for Beijing from the perspectives of possibility and feasibility by taking into account the development objectives and positioning of the urban agglomeration in Beijing, Tianjin and Hebei Province.The following conclusions are obtained: 1) The spatial pattern of the regional market potential of Beijing, Tianjin and Hebei Province centres on Beijing and Tianjin, and the market potential gradually decreases from inside to outside.There are three tiers: Beijing and Tianjin have the greatest market potentials; followed by Langfang, Tangshan, Baoding, Cangzhou and Shijiazhuang, and Qinhuangdao, Handan, Xingtai, Hengshui, Chengde and Zhangjiakou have the lowest market potentials.2) The market shares of Langfang, Tangshan, Baoding, Cangzhou, Chengde and Zhangjiakou are mainly from Beijing and Tianjin and have the closest connections with these two cities. 3) The market potential gaps between Hebei and Beijing and Tianjin are on an increasing trend.Therefore, to achieve coordinated development in Beijing, Tianjin and Hebei Province, Beijing and Tianjin must give full play to their radiating and driving roles and selectively shift some of the industries to Hebei Province.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".