Enrollment Quota Control, Elite Selection and Access to Education in Rural China
Bibliographic record
Abstract
In China, the access to education is determined by not only student’s demand for schooling, but also the allocation of educational resources and the schools’ selection of candidate students. Based on the data obtained from the rural life level and rural social assistance household surveys in four provinces in 2005, the demand-identified bivariate probit model is adopted to identify whether rural youths have the demand for schooling, distinguish between the students’ demand for schooling and the selection of schools, and open out the influence and function of family and social backgrounds on rural youths to acquire the education above junior high school. The empirical research shows that both the deficiency of demand for schooling and the enrollment quota control are important obstacles to restrain the access to education, and the demand for schooling and the elite selection of school all obviously incline to the families and peoples with predominant social backgrounds. The policy implication of this research is that it is imperative under the situation to adopt measures such as improving the demand for schooling of the disadvantaged families and further loosening the enrollment quota control, but the former is more important.
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 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".