Inter-Provincial Accessibility and Economic Linkage Spatial Pattern in Northeast China
Bibliographic record
Abstract
Taking the Northeast China as a study case, this article used the weighted mean travel times as indicators by the travel time cost gravity model to calculate the inter-provincial accessibility and economic linkage intensity among the40 cities. Then, the economic linkage subordination degree was employed to identify the inter- provincial economic linkage direction of the cities. Conclusions are drawn as follows: ①The high values of inter-provincial accessibility are centralized in the Harbin- Dalian economic belt, adjacent region between Eastern Inner Mongolia and Jilin Province,while the low value clusters in the surrounding regions; ②The disparity of inter- provincial economic linkage intensity among cities in Northeast China is remarkable, and inter- provincial economic linkage intensity is highly related to the inter-provincial accessibility, the urban economic linkages conform to the law of distance attenuation; ③Transportation and near- field directivity of inter- provincial economic linkage direction is prominent, the predominant axes of interprovincial economic linkage have formed along the Harbin- Dalian economic belt, and the economic linkage between Eastern Inner Mongolia and three provinces of Northeast China is weak, the same as surrounding regions and other regions. Finally, the driving force of economic linkage spatial pattern in Northeast China can be divided into four parts:regional strategic policy, transportation infrastructure, industrial division cooperation, physical geography condition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".