THE ROLE OF CODE SWITCHING PHENOMENA IN A YOUTUBE VLOG \nBY SACHA STEVENSON \n
Bibliographic record
Abstract
YouTube allows the users to find everything they want to explore supported by audio and visualization.People are also possible to share or upload everything, anywhere, and every time.Those who love to share their videos through their channel are named YouTubers.Sacha Stevenson is a YouTuber who has five hundred thousand subscribers in her YouTube channel.Sacha Stevenson is Canadian but she is fluent in both Bahasa Indonesia and English.Most of her videos in YouTube contains switch language of English and Bahasa Indonesia.The purposes of this study are to find out the types of code switching and to determine the social function or the reason of code switching occur in the related video.In this research, the researcher uses the theory of Myers-Scotton: Types of Code Switching and Markedness Model.The researcher chose descriptive qualitative method to present the data.The data are taken from an observation and the researcher uses note taking technique to find the data.As the result of the study, the researcher finds 34 code switching in form of word, clause, and sentence.The data consist of 14 Inter-sentential Code Switching, 15 Intra-sentential Code Switching, and 5 Tag or Emblematic Code Switching.Based on data analysis, the researcher determine that code switching occur in the video are used to show an emotion or expression, to emphasize messages, and to replace some words that do not exist in English.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".