Evaluation Model of Health Degree and Sustainability of Higher Education System
Bibliographic record
Abstract
Education should be regarded as the foundation for a project of vital and lasting importance. Based on extensive literature review and reference to indicators used in academic rankings made by major institutions, we have collected data which span from 2000 to 2015. After the quantitative processing of indicator data, we divided the entities into three categories by cluster analysis, and used PCA to select several indicators that have a significant impact. After sorting the entities by TOPSIS, we finally selected six indicators. Gross enrolment rate, gender ratios and government investment were used to measure the health of the higher education system, while the proportion of international students and the average age of students at school were used to measure the sustainability of the system. For an entity-specific analysis, we selected Australia, Japan the United Kingdom China and India according to the clustering results. In this section, the fuzzy comprehensive evaluation method was used to score the current higher education system in five countries. The evaluation result met the macro part of the evaluation model. The evaluation model is fully data based with few subjective or arbitrary decision rules. The indicators involved in the model are published statistically by countries around the world so that the model has extremely strong universality. In addition, this model uses lots of methods to make the results more comprehensive and accurate.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".