Bibliographic record
Abstract
ENGLISH: \n \nThis study aims to analyze Sacha Stevenson’s YouTube Channel. This YouTube channel is owned by a Canadian actress who has lived for 17 years in Indonesia. She tried to explain how to be a foreigner in Indonesia and how Indonesian lifestyle was through foreigner’s point of view. Then, since her markets are both foreigners and Indonesians, she used code switching to ease her communication. This study has three questions that become the research questions that are (1 What are the types of code switching used by Sacha Stevenson on YouTube channel? (2 What are the functions of code switching used by Sacha Stevenson on YouTube channel? and (3 What kinds of grammatical patterns used by Sacha Stevenson after committing code switching? \n \nFor achieving the objectives of the study, this study goes through Sociolinguistics. Thus, this study uses Code Switching as the approach. The purposes of this study focus on types, functions and the grammatical pattern of code switching used by Sacha Stevenson. This research used Hoffman’s theory (1991) to analyze the types and functions of code switching. Meanwhile, Sugondho’s theory (1989) was used to analyze grammatical pattern after the code switching. \n \nThe result of this study shows that Sacha Stevenson used all either types or functions of code switching. Those are intra-sentential, inter-sentential and tag switching. While the functions are: talking about a particular topic, quoting somebody else, showing empathy about something, interjection (inserting sentence fillers or sentence connector), repetition used for clarification, intention of clarifying the speech content for the interlocutor, expressing group identity. The kinds of grammatical patterns used on her code switching are complete and incomplete sentences. \n \nIn conclusion, Sacha Stevenson used code switching very well for her communication strategy. Then, for further studies, the next researchers can analyze code switching in other circumstances.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".