A Comparative Study on the Forecast Models of the Enrollment Proportion of General Education and Vocational Education
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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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".