Bibliographic record
Abstract
According to interviews on internal resource allocation models (RAMs) with representatives from ten Japanese comprehensive national universities, the following facts were revealed. Firstly, annual departmental budgets tend to be decided by institutional central offices with rather formulaic approaches and with no official process to respond to departmental requests. However, departments tend to be able to use allocated budgets at their full discretion. Secondly, the categorization of universities (research-led, strong in specific fields and regionally contributing) launched by Japanese Ministry of Education (the MEXT) since the beginning of the third management cycle (FY2016-2021) did not have a significant influence on their RAMs.\nHowever, this policy tool reduced each universityʼs block grant by 0.8-1.6% every year. Under such circumstances, more than a few universities cut the same rate of departmental budgets for research. Thirdly, the redistribution of the block grant among the universities with the assessment of the common performance metrics had a strong influence on their RAMs. All the universities introduced similar budget redistribution schemes with the same metrics internally. However, some universities set a small portion of their departmental budget aside and redistributed it to their departments based on performance metrics. Others did not set it aside and simply added a part of their presidential discretionary budget to specific departments based on the metrics. Overall, this redistribution of the block grant was unpopular since it was suddenly introduced in the middle of the universitiesʼ management cycle, and intruded on institutional budgetary affairs.\nBased on two qualitative comparative analyses (QCA), the following facts were revealed. The universities with comparative advantage in terms of their scale, public resource dependency and research competitiveness have leeway to set a part of departmental budget aside for their internal redistribution and to have more realistic departmental budget formulae responding to departmental demands. However, those with comparative disadvantage do not have leeway to do the same things. All in all, the current block grant redistribution schemes with institutional performance may widen institutional disparities among Japanese national universities.
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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 | 0.016 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".