Bibliographic record
Abstract
Approximately a quarter of a century has passed since “Kanyaku Nihongo (Basic Japanese)” was advocated in the National Institute for Japanese Language. Kanyaku Nihongo was launched as an approach to expand the use of the Japanese language around the world as an international common language. In Basic Japanese, thus created, difficult points of the language for foreign learners were removed and the essential Japanese expressions were presented. These expressions were artificially devised as a means of realizing an efficient acquisition of Japanese as a second language. The increase of immigration became conspicuous at the beginning of the 1990s due to the revision of the Immigration Law. Then the Great Hanshin Earthquake highlighted the importance of providing information to those non-Japanese speakers. “Yasashii Nihongo (Easy Japanese)” was then suggested as an approach to dealing with that problem. “Symbiotic language” is an approach to promoting symbiosis among those with different cultural backgrounds, encouraging acquisition of the second language while ensuring the coexistence of the other languages. I would like to examine the approaches leading up to Universal Japanese through a variety of past suggestions.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.019 |
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".