The Operating Efficiency under the Decreasing Rate of School Students for Public and Private High Schools in Xindian District of New Taipei City: Bootstrap DEA Model
Bibliographic record
Abstract
The goal of this research is to evaluate the low fertility affects school efficiency of high school in Taiwan. Based on Trend Forecasting Model (TFM) and Bootstrap Data Envelopment Analysis (DEA). The empirical results of this research indicate the following results: (1) we utilize our proposed TFM model to study the annual change of student’s population, from 2011 to 2016. The results show that the number of students is indeed reduced year by year in New Taipei City, but the number of declines is not great, from 2011 to 2016. Conversely, the number of student’s declines have great, from 2016 to 2017. (2) The results of the low fertility problem factors caused school of total efficiency to fall below 1. In addition, the government also proposes to raise the fertility policy, for example, the government subsidies schools of tuition. It implies that the larger the school, the economics of scale can be accomplished when outputs expand (such as the number of students (output variable)) and then cause school’s operational efficiency. In contrast, the low fertility problem brings about the number of students (output variable) decreased may directly affect school of scale efficiency and causing the schools total efficiency to fall below 1. The results of this research can also be the reference for educational authorities when formulating policies and regulations for promoting.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".