Urban Integraion Model in Ethnic Autonomous Regions: A case of Yanji Longjing Tumen Area
Bibliographic record
Abstract
The rapid development of urbanization promote the pace of development of all levels of regional in China, and emerge the Phenomenon of urban integration in ethnic autonomous regions. Compared to the general and developed areas,there are several distinctive characteristics of the nationality, the ethnic autonomous and immaturity in ethnic autonomous regions.The paper discuss the specificity of autonomous regions, at the same time elaborated the China's ethnic autonomous regional urban and its particularity in detail. And select the YanLongTu which have the typical characteristics as a case region. In quantitative research, by using AHP method measure the integrating force of the YanLongTu region, and divided the integraing space in three levels by field intensity model. It can be obtained the conclution that YanLongTu integration area is at low level and there is a low field strength between Yanji and Tumen through quantitative analysis. In conclution,the paper proposed three types of control measures about space integrated model, administrative division adjustment mode and regional development mode to promote the development of urban interation in YanLongTu area.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".