Variations of “Large Class Size” in Chinese Elementary Schools and Analysis of Policy Factors
Bibliographic record
Abstract
This research aims to analyze variations of “large class size” in Chinese elementary schools and the influences of education policies on it. Through SPSS21.0, Independent-Samples T Test is adopted to analyze the continuous eleven years” data in “Chinese Educational Statistics Yearbook (2001-2011)”, and the findings are as follows. Firstly, the number of “large class size” in elementary schools presents obvious variations. Secondly, the absolute number of “large class size” in elementary schools shows large fluctuations, while the proportion of “large class size” in elementary schools constantly increases. Thirdly, obvious variations appear in the spatial distribution of the number of “large class size” in elementary schools. “Large class size” in elementary schools has already transferred from urban and rural areas to counties and towns, and the number and proportion of “large class size” in elementary schools in counties and towns has exceeded the sum of that in urban and rural areas. Fourthly, variations of “large class size” in elementary schools result from “closing and merging schools” policy and “two priorities” policy in 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".