Scissors Difference of Socioeconomics, Travel and Space Consumption Behavior of Rural and Urban Households and Its Impact on Modeling Accuracy and Data Requirements
Bibliographic record
Abstract
It is believed that the “scissors difference” of socioeconomics between rural and urban households in typical municipalities of China is significant. This may result in differences in their behavior and has important implications for urban land use and transportation planning policies, as well as related modeling accuracy and data requirements. However, detailed analyses regarding such “scissors differences” between rural and urban groups in China have not been done before. In this study, travel survey data collected from the City of Wuhan in 2008 is used to study if rural and urban households are statistically different in terms of household income, household size, space consumption, highest household mobility and travel distance. A set of statistical tests, such as the Kolmogorov–Smirnov test, Mann–Whitney U test and Kruskal–Wallis H test, are applied to the study data. The study results show that the “scissors difference” is found to be statistically significant in terms of household size (HS), household income (HI), building area (BA) consumed and household mobility (except for travel distance) between rural and urban households. Conversely, analyses applied to travel distance of urban and rural household subgroups (categorized by HS and HI) reveal that the urban and rural counterparts show almost exactly opposite behavior. The study results also suggest that such differences should be explicitly considered in relevant modeling exercises by separately setting up urban and rural household groups, but the number of household groups used should be determined based on a balance between modeling accuracy and data required/modeling workload.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".