Nihon no Daigakuseido--Rekishi to Tenbo (The Japanese University System: History and Future Prospects)
Bibliographic record
Abstract
In 1965, Nagai Michio, education sociologist and one-time Minister of Education, published a short book entitled Nihon no Daigaku—Sangyō Shakai ni hatasu Yakuwari (Japan's Universities: The Role the University Plays in Industrial Society), which begins: As an individual who works at a Japanese university, I have been bothered over the past several years with a question—is the Japanese university good enough? The education offered is limited and, despite the large number of universities (…), there is little research of a global standard. Moreover, many inadequacies cannot be ignored, such as the factionalism in making appointments (Nagai 1965:i). Little has changed in the decades since 1965. Today more than ever, the Japanese university is in a state of crisis. In response to government deregulation, the number of universities has increased dramatically over the past decade or so, by 26%, from 604 four-year institutions in 1998 to 765 in 2008. At the same time, the traditional market, the population of 18-year-olds, which peaked at 2.05 million in 1992, plummeted by a quarter to 1.51 million by 2000, and continues to fall (Askew 2008).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".