Indonesian-English Code-Switching of Sacha Stevenson as a Canadian Bilingual Speaker on <i>YouTube</i>
Bibliographic record
Abstract
Code-switching or language alternation is one of the linguistic strategies that is widely used in bilingual community, including Indonesia. This study attempts to find out the types and reasons of code-switching on YouTube as employed by a Canadian bilingual speaker, Sacha Stevenson. The data used for this study were transcripts of five videos about Indonesian culture taken from Sacha’s YouTube channel. Based on the analysis, there are a total of 313 occurrences of code-switching from Indonesian to English. Poplack’s theory (1980) was applied for the classification of code-switching. The findings showed that the most frequent type is inter-sentential code-switching (42%), followed by intra-sentential code-switching (34%), and the least is tag-switching (24%). This study also explored the reasons for code-switching by applying the theory proposed by Grosjean (1984). It was found that all code-switching occurrences fit into the 11 categorizations of code-switching reasons. This shows a variety of different factors that influence the use of code-switching. The most frequent reason which triggered code-switching is to fill a linguistic need for lexical item, set phrase, discourse marker, or sentence filler (31%). In addition to the 11 reasons proposed by Grojean (1984), another reason for code-switching was found, i.e., to gain popularity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".