Bibliographic record
Abstract
Japanese is ranked as one of the hardest languages to learn, especially for English native speakers (U.S. Department of State, 2009). Despite the challenges involved in learning Japanese as a Foreign Language (JFL), Japanese remains a popular choice among foreign language learners around the world for various reasons including interest in Japanese culture and the language itself (Fukasaku, 2016). In this qualitative study, four international students who participated in a summer JFL program at a private university in Japan were surveyed and interviewed about their motivation of learning Japanese prior to coming to Japan, during their two-month stay in Japan, and after their return to the United States or Canada. The international students’ motivational journeys were analyzed using Dörnyei et al.’s (2015) directed motivational currents (DMCs) model, which exemplifies how surges in motivation could assist foreign language learners in achieving their, past, immediate, and future goals of mastering a foreign language. DMCs were most apparent during their stay in Japan and immediately after returning to the United States or Canada. Their surge in motivation was due to the interactions they had with host family members, their peers who were highly motivated to learn Japanese, and Japanese students. Upon returning the United States or Canada, the students discovered ways to interact with Japanese native speakers to sustain their motivation to learn Japanese. Although many of the international students did not clearly specify how they would be using Japanese in their future, they all had hopes of returning to Japan.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".