Learning Japanese in the Network Society
Bibliographic record
Abstract
Japanese is one of the most difficult languages to learn for English-speaking students, but emerging technologies are making revolutionary changes that help to ease the learning curve. Learning Japanese in the Network Society addresses current issues of computer-assisted language learning (CALL) shared by language-teaching professionals in the new global network society. Focussing on teaching and learning Japanese as a second language, this collection emerged from previously unpublished presentations by leading Japanese scholars at the International Conference on Computer Technology and Japanese Language Education held at the University of Toronto. This book is a comprehensive resource on the current status of CALL and is essential reference material for any language-instruction course. With Contributions By: Kanji Akahori Jim Cummins Masako O. Doulgas Yoshiko Kawamura Kazuko Nakajima Akifumi Oikawa Hiroko Chinen Quackenbush Yuri Shimizu Yoko Suzuki Yasu-Hiko Tohsaku Michio Tsutsui Hilofumi Yamamoto
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".