Dynamic Effects of the Chinese GDP and Number of Higher Education Based on Cointegrating
Bibliographic record
Abstract
This study’s objective was to the issue for the impact between regular higher education scale and GDP in China. We integrate Unit Root Test, Cointegration Test, Vector Error Correction Model (VECM), Variance Decomposition, etc. The sample is to use the annual data of GDP and number of Students Enrollment of Regular Institutions from 1952 to2004. Empirical results show that there is co-integration relation between GDP and number of Students Enrollment of Regular Institutions and economic growth can affect higher education scale and the contribution of education to economic growth is increasing gradually. To achieve good interaction between higher education and economic growth, the advice is that make scientific policy of regular higher education scale’s expansion.Key Word: Gross Domestic Product (GDP); Granger causality test; Vector Error Correction Model; Cointegration Test; number of Students Enrollment of Regular Institutions
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 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".