Bibliographic record
Abstract
Administrative reform has been high on the political agenda in Japan for quite some time now, and, as in most other countries, the introduction of information technology (IT) to administration offices is seen as a vital part of this. However, Japan has long been slow to join in the development of e-government features, which have been in successful operation for quite some time now in the U.S., Canada, and the Scandinavian countries. In Germany, the federal government started the initiatives Bund Online 2005 and Media@Komm to promote e-government services on all levels of government. The Japanese government launched the “e-Japan Initiative” in January 2001 to help Japan catch up with the development of the so-called “IT Society.” Part of this initiative is also the promotion of e-government services. The aim of this initiative is to offer 98% of national government services online by the end of 2003. A large number of services have already been introduced by the national government and on the local level. One of the main obstacles for a broad usage of e-government functions, though, is the secure identification of the parties to a transaction and the replacement of requirements for written declarations by equally secure electronic methods. The Japanese Digital Signatures Act of 2001 opened the way for national standards in electronic identification through a qualified digital signature similar to that laid out in the EU directive of January 2000. This is also accepted as a standard in the area of administrative procedures, and the ministries concerned have been authorized by the national government to alter their respective laws to allow electronic communication with the use of digital signatures. A Law on Municipal Signature Providers and Online Administrative Procedures was introduced to the Diet in June 2002, and an already-passed alteration to the Notary Act allows Japanese notaries to digitally certify certain documents. Another problem to be solved is the security of personal data. This has been illustrated by the fierce discussion surrounding the introduction of a national population registry in Japan in 2002. Together with a national registry, the idea of connecting the traditional personal seal system with digital signatures might also come a little closer to reality.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".