Research on Effects of Chinese Current Tax System Adjustment on Income Distribution of Urban Residents
Bibliographic record
Abstract
This article analyzes the adjustment effect of Chinese current tax system on income gap among urban residents, using statistical and econometric research methods, and a series of the Gini coefficient, income equality index and etc. to calculate and compare income disparity of urban residents in the existing tax system. The research result shows that the existing tax system has hardly any effect on income distribution of urban residents. Thus the last part of this article puts forward some suggestions to the government on how to reform currently tax system in order to improve people's livelihood, and promote harmonious development. Key words: Tax system; Income gap; Adjustment effects Resume: Cet article analyse l'effet de l'ajustement de l'actuel regime fiscal chinois sur l'ecart des revenus entre les habitants urbains, en utilisant des methodes de recherche statistique et econometrique, ainsi qu'une serie de coefficients de Gini, l'indice de l'egalite des revenus afin de calculer et de comparer les disparites de revenus des residents urbains dans la l'actuel regime fiscal. Le resultat de la recherche montre que le systeme fiscal actuel n'a guere d'effet sur la repartition des revenus des residents urbains. Ainsi, la derniere partie de cet article met en avant quelques suggestions au gouvernement sur la facon de reformer l'actuel systeme fiscal en vue d'ameliorer la vie du peuple, et de promouvoir un developpement harmonieux. Mots-cles: Systeme fiscal; Ecart des revenues; Effets d'ajustement
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".