Regression Analysis Research on the Impact of Urbanization on Farmers’ Consumption Structure
Bibliographic record
Abstract
The status of research on the impact of urbanization on farmers’ consumption structure conducted by the domestic and international scholars is described in the paper; and the argument is supported by exploration and analysis that the urbanization has exerted an influence on farmers’ consumption structure. Furthermore, by concretely exploring the related data model constructed in the research, the following achievements are made: along with the advancement of urbanization, the proportion of three categories including food and clothing in farmers’ consumption structure turns on a downward trend, while the proportion of housing, transportation, and other five categories are in an upward trend; in the farmers’ consumption expenditure, the medical and health expenditure is significantly affected by urbanization, while urbanization only has a little influence on food expenditure. On the basis of the conclusion in this paper, suggestions are put forward which include promoting the urbanization rate, creating a better condition for the development of the rural residents, improving the basic social security system and perfecting a series of policies that stimulate rural consumption including the “home appliances going to the countryside”, “mobile phones going to the countryside”, and “cars going to the countryside” etc..
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".