Bibliographic record
Abstract
Education plays an important role in social life and human society. However, in the world, due to different levels of economic development, traditional culture, values, policies and regulations, historical development and other factors, there are huge differences in the higher education system of different countries. It requires us to develop a model that can be used to evaluate the health of higher education systems in any country. We start from two angles. First, from a macro perspective, the higher education system of a country is rated by collecting relevant data of different regions, cultures and countries with different economic development in the world. Another Angle is from the perspective of classification, from the macro point of view of the country's higher education system to grade. Although the final result this method is intuitive, but only from the final score to assess its higher education system is very one-sided, so we will have the same characteristics of countries get together for a class, this not only can compare for different categories of countries, in order to optimize the its higher education system, but also the original evaluation model with partial faults are optimized. We applied the above model to 17 countries around the world, evaluated them reasonably, and selected one country with room for improvement in its higher education system -- Canada.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".