Ranking Japan’s Institutions of Higher Education, 2017: A Comparative Analysis
Bibliographic record
Abstract
The present study examined the 2017 Times Higher Education annual rankings for Japanese institutions of higher learning. Based on the analytic model as mapped out previously using Canadian data, we offered a similar protocol for the top 100 institutions of higher education in Japan. Three analyses showed that: (a) overall rank correlated with individual index ranks for 9 of the 13 indices, (b) the schools appearing among the top institutions overall ranked significantly better on 8 of the 13 indices compared to schools appearing among the bottom institutions overall, and (c) schools were empirically grouped into four meaningful families or clusters whose constituent members shared a comparable profile of indices. We offer a juxtaposition of the present results to annual evaluations from Canada’s institutions of higher learning. The wider implications include an international comparison of institutions of higher learning, a proposed analysis protocol that Japanese education administrations may further pursue, and a categorical breakdown of educational institutions in Japan. Directions for future research are outlined.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".