Regional Difference in Distribution of China's Colleges and Universities and Its Impact on the Equality in Access to Higher Education
Bibliographic record
Abstract
The inequality in higher education recruitment chances among various regions in China has been a much talked about subject and has triggered a number of discussions in recent years. Yet the systematic empirical study on this topic has not been carried out. This paper is an empirical effort to study the nature and reasons of inequality in access to higher education in China. The key questions asked in this study are, What is the impact of the imbalance in the regional distribution of higher education institutions on equal educational opportunities for students? And How has the regional distribution of higher education institutions formed and evolved in China? This paper traces back the historical process for the formation of regional distribution pattern of China’s higher education institutions, and through statistical analysis, discusses the influence of the imbalance in regional distribution of higher education institutions on students’ chances for higher education. The authors’ main findings are that, (1) The imbalanced regional distribution pattern of China's higher education institutions was mainly formed during the readjustment of departments and colleges in 1950s. Later, although the overall number of colleges and universities during the periods of Great Leap Forward (1958-1960) and the Cultural Revolution (1966-1976) fluctuated, the basic distribution pattern had not changed too much until 1990s. (2) There are two main reasons for the inequality in the chances for the students to be recruited by colleges and universities among different regions in China. One is the imbalance in regional distribution of colleges and universities, the other is the enrolment system with the planned number of students to be enrolled being based on provinces. The latter, in an institutionalized way, transforms the imbalance in regional distribution of colleges and universities into the difference in recruitment mark lines in different regions, and thus leads to the inequality in chances for being recruited by colleges and universities. Key words: China's higher education, Inequality in access to higher education, imbalanced regional distribution. Resume: L’inegalite de chances dans le recrutement de l’education superieure parmi les differentes regions en Chine a ete un sujet beaucoup parle et a provoque de nombreuses discussions dans les dernieres annees. Mais une etude empirique et systematique sur ce probleme n’a pas encore ete entreprise. Cet article est un effort empirique pour etudier la nature et les raisons de l’inegalite de l’acces a l’education superieure en Chine. Les questions clefs posees dans cette etude sont comme suite : quel est l’impact du desequilibre de la distribution regionale des institutions d’enseignement superieur sur les opportunites educatives des eleves? Comment la distribution regionale des institutions d’enseignement superieur a forme et evolu en Chine? Cet article decrit le processus historique de la formation du modele de la distribution regionale des institutions superieures de Chine, et discute, par l’analyse statistique, les influences du desequilibre dans la distribution regionale des institutions d’enseignement superieur sur les chances des eleves pour l’education superieure. Les conclusions principales que l’auteur a tirees sont: (1) le modele de la distribution regionale desequilibree des institutions d’enseignement superieur a ete forme pendant le rajustement de departements des instituts et universites dans les annees 1950. Apres, bien que le total des institutes et universites durant la periode du Grand Bond en avant (1958-1960) et la Revolution culturelle (1966-1976) ait fluctue, le modele de distribution fondamental n’a pas beaucoup change avant les annees 1990. (2) Il y a deux raisons pour expliquer l’inegalite des chances pour les eleves d’etre admis par les instituts ou universites parmi les diverses regions chinoises. L’une est le desequilibre de la distribution regionale des institutions ; l’autre est que le systeme de recrutement qui definit le nombre des etudiants recrutes selon les provinces. La deuxieme, d’une maniere institutionalisee, transforme le desequilibre de la distribution des instituts et universites en difference du niveau de notes dans de differentes regions, et conduit a l’inegalite des chances d’etre recrute par les instituts et universites. Mots-Cles: education superieure de Chine, inegalite de l’acces a l’education superieure, distribution regioale desequilibree
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 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".