Code-Switching in Persian/English and Korean/English Conversations: with a focus on light verb constructions
Bibliographic record
Abstract
This study capitalizes on the hypothesis that typologically similar languages follow similar patterns for code-switching. Persian and Korean have similar syntactic structures. For example, both languages have the same canonical word order as Subject-Object-Verb. One of the most productive structures in both languages is light verb construction (LVC) in which an active/patient-denoting verbal element appears as the object or complement of the light verb. Our data in Persian/English and Korean/English code-switching reveal that bilingual speakers of Persian or Korean follow similar patterns when code-switching, especially in light verb constructions. In Persian and Korean bilingual light verb constructions, an L1 light verb or its inflected form is attached to an English noun, adjective, adverb, preposition, or verb. The code switching data used in this study were collected from separate spontaneous conversations involving five Iranian-Canadian and five Korean-Canadian undergraduate students living in Canada. A one-hour conversation for each group was recorded and was transcribed by a native Persian and a native Korean speaker for further analyses. We examine the pattern of code-switching in light verb constructions within the context of other
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".