Society5.0 時代における大学院教育 : その将来像と課題 第52 回（2024年度）研究員集会の記録
Bibliographic record
Abstract
Summary Report of the Research Institute for Higher Education Annual Study Meeting, 2024 The 52nd Researchers' Meeting was held on November 8, 2024, with the theme of "Graduate School Education in the Society 5.0 Era: Its Future Vision and Challenges." This researchers' meeting was co-hosted by the Chugoku-Shikoku Branch of the IDE University Association, of which the President of Hiroshima University serves as the branch chair. In the 2021 "6th Science, Technology and Innovation Basic Plan," Society 5.0 was redefined as "a society that is sustainable and resilient, ensures the safety and security of the people, and enables each individual to realize diverse well being." In order to realize such a society, strengthening research capabilities that will open up the frontiers of knowledge and become a source of value creation was presented as one of the important policies. In addition, the "Basic Act on Science, Technology and Innovation," which came into effect in April 2021, stated that "integrated knowledge" that combines all "knowledge," including humanities, social sciences and natural sciences, will contribute to a comprehensive understanding of humans and society and problem solving. In this way, the nature of knowledge is becoming important in society, and expectations for graduate schools, which are at the forefront of knowledge, are increasing. Despite this, the number of Japanese graduate students in master's and doctoral programs in humanities and social sciences, and in doctoral programs in science, engineering, and agricultural sciences has been declining in recent years. It is difficult to say that Japanese graduate schools are living up to expectations. At this year's researchers' meeting, the following experts involved in graduate school practice and research provided information on the expectations, current situation, and challenges for graduate schools in countries around the world, especially Asian countries including Japan, and how they are trying to address these challenges. Professor Glen Jones of the University of Toronto and Professor Hiromi Yokoyama of the University of Tokyo gave keynote speeches, and information was provided by Professor Masanori Hanawa of Yamanashi University, Professor Jung Cheol SHIN of Seoul National University, Associate Professor Wenqin Shen of Peking University, and Professor Mari Kawamura of the Ministry of Education, Culture, Sports, Science and Technology's National Institute of Science and Technology Policy. In response to these keynote speeches and information provided, Professor Hisakazu Matsushige, Professor Emeritus of Osaka University and currently Professor at Takamatsu University, and Professor Yosuke Yamamoto, Professor Emeritus of Hiroshima University, provided comments. Many people participated in the meeting both in person and online. We would like to thank all the speakers and everyone who took time out of their busy schedules to take part in the discussion. We have compiled a record of the day's proceedings as a publication by the Hiroshima University Center for Research and Development of Higher Education. We hope that this book will contribute to the development of graduate schools in Japan.
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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 | 0.003 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".