A Comparative Study on the Forecast Models of the Enrollment Proportion of General Education and Vocational Education
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
Predictive research on the enrollment proportion of general education and vocational education is crucial to optimizing the regional talent structure and industrial structure adjustment. The reasonable enrollment proportion of general education and vocational education also plays an important role in the adjustment of the overall employment structure and the development of the regional economy. Therefore, it is imminent to seek a more accurate and reliable prediction model of the enrollment proportion of general education and vocational education. Based on the grey prediction model, exponential smoothing model, ARIMA model and BP neural network, and with the data of the enrollment proportion of all regions in China from 2010 to 2018 as the data sample, the enrollment proportion of each region in 2019 is predicted. By comparing the predicted values with the real values, it is found that the exponential smoothing model has the best accuracy and stability for the enrollment proportion of general education and vocational education forecast. Exponential smoothing model is used to predict the number of high school enrollment and vocational education enrollment, which is of great significance to ensure the reasonable structure of human resources in various regions and promote the coordinated development of the education system.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it