Bibliographic record
Abstract
The Mission of NINJAL 国立国語研究所は日本語に関係する学術研究の進展を牽引することをミッションとします。 そのために以下の取り組みを重点的に進めます。The mission of the National Institute for Japanese Language and Linguistics (NINJAL) is to advance academic research related to the Japanese language.NINJAL will achieve this by prioritizing the following initiatives:-言語研究のための高品質な言語資源の開発など，日本語・日本語教育研究のインフラ整備 を大規模かつ組織的に推進し，利用を促進します。 -Promoting the systematic development and widespread use of infrastructure for research on the Japanese language and its education, including large-scale and high-quality language resources.-日本語ならびに日本語と関連する諸言語について，諸科学と連携しながら先端的な研究を 実施して新たな研究領域を開拓し，国内外の研究ネットワークを構築します。 -Conducting cutting-edge research on Japanese and related languages in collaboration with related sciences, pioneering new fields of research, and building both domestic and international research networks.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.600 | 0.730 |
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".