Multilevel and Spatially Heterogeneous Factors Influencing Poor Households’ Income in a Frontier Minority Area in Northeast China
Bibliographic record
Abstract
Increasing the income of poor rural households is essential for the realization of China’s goal of sustainable development, which entails inclusive and equitable development and reducing the developmental gap between urban and rural areas. We conducted a case study of Wangqing County, a frontier minority area in Northeast China to examine spatial patterns and income differentials among poor rural households in this area. We quantified existing associations between household‐level and environmental‐level characteristics and income by applying hierarchical linear models. We subsequently applied Geographically Weighted Regression to analyze the spatial heterogeneity of the environmental‐level variables and develop an understanding of the interaction mechanism of influencing factors. The results revealed that the distribution of villages, where income levels were similar, showed significant spatial agglomeration characteristics. Our findings also provide empirical evidence that household‐ and village‐level characteristics together determine the income of poor households, but that household‐level characteristics determine destitution to a greater extent than environmental characteristics. More specifically, the sex, health condition, and labor capacity of the household head, household size, the dependency ratio, social welfare, and off‐farm work are significantly associated with household income. At the environmental level, arable land, the distance to the county center, and the average altitude had spatially heterogeneous impacts that varied in direction and intensity. This systematic study provides a more comprehensive and integrated understanding of the factors influencing the income of poor households in a frontier minority area in Northeast China.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".