School District System: A Realistic Way to Crack the Problem of Compulsory Education School Selection for Students in China
Bibliographic record
Abstract
Looking through “nearest school entrance” policy evolution in China for nearly 30 years, the inherent logic of its value pursuit should act as receive compulsory education equally, whereas neighborhood entrance is just a path to realize the equality. However, under the circumstance of huge gap between different schools in China, neighborhood entrance policy will not necessarily lead to education fair, but only a sub-optimal choice. School district system is a bold breakthrough of institutionalized “nearest school entrance” policy. Through building rational “students flow” system, It meets the individualized education demand of parents, and ensure education equality. Therefore, school district division standard should transform from adhering to convenient education administrative to the free, flexible and convenient choices for students to choose school. School district division should also stick to the coordination of three principles: “nearest school entering, student needs and school developments”. The policy should also be rooted in multiple school division on the basis of relevantly balanced school resource, appropriate promotion of students flow, exploration of scientific district management mode, changing from the “gap cooperation” to “difference cooperation”, so as to build a connotation development path.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".