Spatial inequality in children's schooling in Gansu, Western China: reality and challenges
Bibliographic record
Abstract
China has experienced considerable economic growth following the economic reforms of 1978, while simultaneously facing dramatic increases in regional inequality. China is becoming a polarized society—a phenomenon that is at the heart of a multitude of serious problems that are threatening sustainable development, as well as social cohesion within the country. Among the key reasons for this polarization are the quality of and accessibility to basic education for children. Since the establishment of the law for nine‐year compulsory education in 1986, children's education has progressed remarkably in most parts of China. It has, however, remained persistently problematic in the western provinces, particularly in remote regions, rural areas and minority communities. Even though some studies on child education in China have been carried out, very little existing research examines spatial inequality in children's schooling or accounts for the importance of sociocultural and geographic contexts. Using the example of Gansu, one of the poorest provinces in Western China, our research emphasizes the two main aspects that have led to high nonschooling rates for children: an unfavourable sociocultural milieu and inadequate educational resources .
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".