Simulation and optimization of residents’ consumption behavior change and consumption upgrading path under multi-factor conditions
Bibliographic record
Abstract
Increasing urbanization has led to large changes in residents' consumption behavior, but due to a variety of factors, the overall level of residents' consumption is low, so that it cannot play its role in promoting economic growth.This paper selects the panel data of China Household Finance Survey from 2011 to 2019, and empirically analyzes the impact and path of changes in residents' consumption behavior on consumption upgrading by constructing structural equation model and fixed effect model combined with STATA software.The study shows that changes in residents' consumption behavior and its dimensions can promote residents' consumption upgrading, and there is regional heterogeneity in the impact of changes in residents' consumption behavior on residents' consumption upgrading, and its promotion effect on residents' consumption upgrading is stronger in the developed regions in the Middle East.Based on the above findings, this paper puts forward feasible suggestions on how to optimize residents' consumption behavior to better promote residents' consumption upgrading.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".