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.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 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 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".