Bibliographic record
Abstract
Technology in an aim to "strengthen and enhance the education and research functions of graduate schools, to foster highly creative young researchers who will go on to become world leaders in their respective fields through experiencing and practicing research of the highest world standard."Basic human needs, environmental pollution, disasters, and how to secure the selfsustained capacity to deal with these issues are major challenges in Asian megacities.Yet attempts to deal with these problems in the past several decades have been a string of failures.One main reason for this is the rapid expansion of cities.More importantly, however, the introduction of technologies and systems for dealing with these risks has been carried out bit by bit, and even where technologies and systems were adopted, the importance of providing human resources and communities to manage them was overlooked.Based on this awareness, our program is founded on civil engineering, architecture, environmental engineering, and disaster prevention studies while it is based on a thoroughly field-oriented approach.By focusing concentration on the complementary co-evolution of engineering technologies, urban management, and systems design, we will elevate the elemental studies we have developed until now toward a more comprehensive discipline that encompasses urban management strategies and policies, and promote research and education based on this new discipline.Specifically, we have started conducting activities that include working together with universities, research institutions, and private enterprises at overseas bases located in seven countries throughout Asia, as well as in our headquarters in Kyoto to foster doctoral students per academic year.We look forward to contact from those of you interested in this GCOE program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.408 | 0.354 |
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".