Bibliographic record
Abstract
This study addresses language use by Korean-English bilinguals in self-addressed writing. Internal use of language sheds light on the speaker-related factors in language use and code-switching. While code-switching is a highly popular topic in bilingualism, it has been underexplored in self-addressed writing, where the interlocutor and domain factors are neutralized. To fill in this gap in research, the current study poses the following research questions: 1. Does picture priming trigger the use of a language associated with the culture represented in the picture (in a shopping list scenario)? 2. What languages are employed by the participants in self-addressed task-based writing (a to-do list) and why? 3. What are the code-switch patterns in the writing tasks? The study employs a mixed-methods approach with quantitative and qualitative elements that come from the analysis of three writing tasks, a survey, and answers to an open-ended post-writing question produced by 34 Korean-English bilinguals residing in Canada. The results show that participants did not necessarily code-switch, and some of them used only one language. The number of English words in the tasks correlated with the length of residence in Canada, the language the participants were most comfortable with, and English fluency. Picture priming increases the production of words in the language associated with the picture. Lack of priming decreases code-switching. The participants explain their language choice by the context, their experience with the tasks, language preference, cognitive load, and frequency of language use.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".