Variations of “Large Class Size” in Chinese Elementary Schools and Analysis of Policy Factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.012 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it