Bibliographic record
Abstract
The goal of this paper was to explore the directions for a Japanese Qualifications Framework (JQF) through the collationof Japanese Government expert viewpoints.This study used a qualitative case study design involving interviews with 15 Japanese government officials. It was foundthat Japan continues to haveproblems with academic degrees and licensing framework and system. Many Japanese government experts believed that Japan needed a qualifications framework and system that could function both domestically and internationally, however, Japan has an insufficient qualifications framework and system which has led to weak competitiveness for Japanese experts. To resolve these issues, a Japanese qualifications system needs (1) to have pathways toward higher skill levels from work-based experience to higher education and (2) to broaden pathways allowing for the transfer of Japanese domestic qualifications to international framework qualifications.It is concluded that to build these pathways, National Qualifications Framework is needed as a common language and a basic framework to make the qualifications more transparent and to align domestic and international qualification standards. The Japanese government should enter into a discussion about JQF seriously with stakeholders in education, industry, and government with the aim of improving higher education programs for experts to ensure domestic and international competitiveness.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".