Construction of Tianjin Fashion Industry Data Analysis System under the Framework of International Consumption Center City
Bibliographic record
Abstract
In the context of building an international consumption center city, it is of great significance to further study the competitiveness of the fashion industry and effectively grasp the direction and focus of the development of the fashion industry in order to promote the construction of an international consumption center city.The study adopts the entropy weight-TOPSIS method to measure the competitiveness of Tianjin's fashion industry from 2020 to 2023, and compares it with typical provinces in order to have a comprehensive understanding of its fashion industry competitiveness level.Then, the spatial structure characteristics of the distribution of fashion industry facilities in Tianjin were further explored through the kernel density analysis method and the radius of gyration analysis method.Finally, Ripley's K function is used to calculate the level of agglomeration and the range of the most significant agglomeration scale of each type of fashion industry, which summarizes the distribution characteristics of strategic fashion industries at the overall level.Horizontally, the competitiveness level of Tianjin's fashion industry shows an upward trend from 2020 to 2023, and vertically, the competitiveness level of Tianjin's fashion industry is ranked in the middle range of the country, with a certain gap between it and the strong provinces such as Jiangsu, Shandong and Guangdong.The most significant agglomeration scale of the new generation electronic information technology industry is 22,000 meters at maximum, and its DiffK value also reaches 13,317.938.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".