Code Switching and Code Mixing in Indonesia: Study in Sociolinguistics?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Code-switching and code-mixing are part of the Study of Bilingualism in Sociolinguistics which have become a very popular language, it is to be an influence for smooth communication in Indonesia because many Batak Toba and Mandailing speakers who realize code-switching and code-mixing using other languages such as Indonesian and English in a particular conversation in everyday life. It is thus necessary to study for smooth communication and prevent misunderstandings and prevent deaths language speakers. The method that is applied in this reseach is qualitative research, which used interview techniques, questionnaires, observations and records for taking the data. So, the research was conducted by taking the real data from communities in North Sumatra, Indonesia. From the research, we found 75 expressions from 3 places of the research. They are City of Medan, Siantar and Region of Mandailing Natal. In addition, code switching and code mixing in Indonesia have been divided into three classes. They are word class, phrase class, and sentence class. Interestingly, the word level is the highest number that is occurred in Indonesia, which reached 57.3% from all the data. Then, for the second and the third positions are phrase and Sentence levels, which reached 40.4% and 17.3% respectively.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it