Border effect on migrants’ settlement pattern: Evidence from China
Bibliographic record
Abstract
Migrants' settlement is an emerging topic, especially in underdeveloped countries with massive internal migration. This study is a response to the pressing need of theorizing the emerging migration issue and enriching the conceptual approach in migration studies. From the perspective of the border effect, we propose a conceptual model of population redistribution. It reveals that the border effect on migrants' settlement pattern presents an inverted U-shaped change that the migrants' settlement pattern evolves from low-level balance to high-level balance across space. Besides, border effect plays different roles in settlement patterns of inter-regional migrants, intra-regional migrants, and the difference between inter-regional migrants and intra-regional migrants. Using an extended Barro regression, China's case validates the Barrier-Ⅱ stage in the conceptual model, in which the border effect tends to be weakened and there is a more even distribution of migrants who settle in the destination across space at the regional level. Economic disparity and social/cultural differences produce the border effect, and the border plays a more influential role in the distribution of the difference between inter-provincial migrants and intra-provincial migrants.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".