Governance, Migration and Educational Rights: A Policy Analysis of Migrant Children's Educational Access in Sichuan, China
Bibliographic record
Abstract
ABSTRACT This paper examines how provincial‐level policy innovation in China is reshaping educational access for migrant children, focusing on Sichuan's 2024 Implementation Plan for Strengthening the Care and Protection of Migrant Children. Rather than viewing the 2024 Plan in isolation, the analysis locates it within the evolving trajectory of China's hukou household registration reform and rural–urban labour migration. By tracing the link between national policy changes and demographic responses, it highlights how macro‐level reforms created new conditions for local policy experimentation. It analyses both the Plan's emergence and its structural components. The plan is interpreted as a locally grounded effort to integrate previously disjointed services into a coordinated system, shaped by both top‐down directives and local problem‐solving. This case illustrates how provincial initiatives can function as adaptive strategies to national policy shifts, yet remain embedded in structural and legal constraints. The paper further reflects on the challenges of applying Western‐derived policy frameworks in non‐Western contexts. Drawing on comparative scholarship in postcolonial political theory, it calls for more contextually attuned approaches—ones that recognise the distinctive logic of Chinese governance, rather than evaluating it through universalist assumptions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".