Japanese Language Proficiency, Social Networking, and Language Use during Study Abroad: Learners’ Perspectives
Bibliographic record
Abstract
Abstract: This study examines the self-perceived speaking proficiency development of 204 learners of Japanese who studied abroad in Japan and analyzes connections between self-reported social network development, language use, and speaking development. Learners perceived that they gained the most in areas associated with the intermediate and advanced levels of the ACTFL (American Council on the Teaching of Foreign Languages) Speaking Proficiency Guidelines and the least in novice and superior-level abilities, largely because of a ceiling effect – learners were highly capable of novice-level tasks before study abroad and therefore had little to gain. Regression analysis indicated that self-reported pre-departure proficiency level, dispersion (number of social groups in which a learner participated), time spent in Japan, total time spent speaking Japanese, time spent speaking with native-speaker friends, and time spent speaking English with native speakers of Japanese predicted 44.6% of the variance in the perceived gains of study abroad in speaking proficiency. Self-reported pre-departure proficiency level and time spent speaking English with native speakers of Japanese were negative predictors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".