The TOPSIS Analysis on Regional Disparity of Economic Development in Zhejiang Province
Bibliographic record
Abstract
This paper is aimed to evaluate the regional disparity of economic development in Zhejiang Province. According the principals of the criteria and the practical situation of the 11 cities, this paper makes the analysis by TOPSIS method through ten indicators, with the data from 2007 to 2009. This evaluation shows that there exists regional disparity of economic development among the 11 cities. Further, this paper investigates the reasons behind the disparity and discusses those cities’ roles in the whole province. Key words: Regional Disparity; Economic Development; TOPSIS Resume Cet article est destine a evaluer les disparites regionales de developpement economique dans la province du Zhejiang. Selon les principes de criteres et de la situation concrete des 11 villes, ce document fait l'analyse par la methode TOPSIS travers dix indicateurs, avec les donnees de 2007 a 2009. Cette evaluation montre qu'il existe des disparites regionales de developpement economique parmi les 11 villes. En outre, ce document examine les raisons derriere la disparite et discute des roles de ces villes dans toute la province. Mots cles: Disparite regionale; Developpement Economique; TOPSIS
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".