The Empirical Study on the Different Effects on Urban and Rural Consumption by Urbanization in China
Bibliographic record
Abstract
Consumption and urbanization mainly by transferring labor force are important variables in China. The article puts the two into analysis to consider the quality of the urbanization, selecting the data of urbanization rate, average consumption of urban and rural residents, using econometric tools of co-integration analysis, ECM and Granger causality test, and after that the paper finds the effects on urban and rural consumption by urbanization are extremely different: The urbanization rate has a long-term equilibrium relationship with urban residents’ consumption, but does not have this relationship with rural one. The dynamic relationship between the urban consumption and urbanization is that the former makes the latter rise up, then the two promote each other, and finally the latter makes the former go up remarkably. Further more, although urbanization influences urban residents’ consumption obviously, yet there is a delayed effect. So we should shift the production-factor-oriented urbanization model to people-oriented one, boost the supply of public goods, focus on the development of agricultural sector, increase the income of rural households to expand the consumption of rural residents and improve their qualities of lives.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".