Bibliographic record
Abstract
This paper explores the geographic distribution of all colleges and universities in Japan and examines thestructural characteristics of the higher education system within its 47 prefectures. The results revealed severalinteresting findings. First, there are at least one national, public, and private university in almost all prefectures, and the 47subsystems of higher education look much the same. However, the number and variety of faculties differamong private universities. Secondly, prefectures with many faculties at national and public universities alsohave many faculties at private universities. Conversely, in regional prefectures, the number of faculties atnational, public, and private universities are small, resulting in limited specialization in the human resourcesbeing trained. Thirdly, the number of specialized fields in which human resources are trained at the facultylevel for 39 regional prefectures (excluding the Tokyo metropolitan area, the Kansai region, and AichiPrefecture) ranges from 5 to 12, with the most frequent value being 10. However, 13 prefectures (Yamagata,Gunma, Nagano, Kagawa/Akita, Fukushima, Wakayama, Oita/Fukui, Mie, Saga/Shimane/Tottori) have 9 orfewer specialized fields. More than a quarter of the prefectures are unable to train human resources in allfields within their own prefectures. The population of 18-year-olds is decreasing, and the number of private universities is expected todecrease in the future. In regional prefectures, it is anticipated that a limited number of national, public, andprivate universities will need to make effective use of educational resources.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".