Secondary Education (High School) in China
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
Secondary education or high school in China refers to the general upper secondary educationUpper secondary education, which aligns with the International Standard Classification of EducationInternational Standard Classification of Education (ISCED) (ISCED) Level 3. In China, this stage typically includes students aged 15 to 18 years old, corresponding to grade levels 10 to 12, and excludes vocational educationVocational education. This chapter reports the current stage of China’s high school education, focusing on the key themes of educational effectiveness and resource allocation. It utilizes data sourced from the Organization for Economic Cooperation and Development (OECD)Organization for Economic Co-operation and Development (OECD), the 2018 PISA database, and official statistics from the Ministry of EducationMinistry of Education (MOE) of China (MOE) and other countries. The data indicates that, in international comparisons, Chinese high school students lead in gross enrollmentEnrollment rates, graduation ratesGraduation rate, and academic performancePerformance. Notable accomplishments in educational infrastructureInfrastructure, such as science labs, multimedia-equipped classrooms, and widespread Wi-Fi accessAccess in schools, are also highlighted. Nevertheless, compared to many developed countries, China faces challenges in several key indicators, including the total spending per full-time student, the proportion of teachers holding a master’s degreeDegreemaster’s degree or higher, and the percentage of students gaining admission to top 4-year universities. This chapter also presents best practices and inspiring stories and within China’s high school education, and it examines recent trends through the lens of the latest research, national policies, and recommendations for the future.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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