Analysis on the level of higher universities in different countries using entropy weight method and analytic hierarchy process
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
The higher education system is an important factor in measuring the education level and national strength of a country. A healthy and sustainable higher education system can effectively improve the country's competitiveness. The establishment of a complete and unified model for evaluating the pros and cons of the national higher education system is the basis for improving the level of the higher education system. In this article, we will build the relevant model in four steps. The first step is to search for documents and data, and use data on factors such as knowledge protection in multiple countries, higher education enrollment rates, education system quality, student Internet access, government education expenditures, the number of citations per paper, and the value of degrees. Build a model based on it. And standardize all data. In the second step, since the factors may have a certain correlation, the principal component analysis method is used to convert the equi-related variables into another set of unrelated variables and establish a model. The third step is to analyze the health and sustainability levels of higher education systems in Canada, France, Germany, Italy, Japan, Russia, the United Kingdom, the United States and other countries based on this model, and obtain rankings and comprehensive scores. The fourth step is to use the entropy method to calculate the specific weight of each factor. Select one of the countries to reform the most weighted factor. Import the reformed data into the model again, observe and analyze its changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 it