Bibliographic record
Abstract
This study examined the outcomes of study abroad, and further investigated why some learners make more language contact and/or proficiency gain during their study abroad than others. The research participants were 25 second-year Japanese university students in a private university in Tokyo, learning English in a 15-week study abroad program in Canada. This study generated three research questions. Research question one sought to find both linguistic and non-linguistic outcomes of a study abroad. Research question two had two parts: investigating variables associated with postreturn English proficiency test results, and exploring variables associated with while-abroad language contact. Finally, in research question three, qualitative data were used to find interpretations and explanations into the variables identified by quantitative data, especially focusing on the amount of while-abroad language contact. The variables used in the study include affective factors e.g., motivation, willingness to communicate, language anxiety, self-perceived English skills, and homesickness. In addition, learner profiles, e.g., previous study abroad experience, the number of English courses taken prior to study abroad, the amount of language contact prior to study abroad, and English proficiency, were considered. This research employed mixed methods, and the following sections summarize a number of findings from both the quantitative and qualitative analysis.
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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".