Code Choices in Marriage Discourse Preach: A Sociolinguistic Analysis
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
As a global phenomenon, language contact that causes code-mixing (CM) and code-switching (CS) happens in many situations, including wedding parties. This study analyzed the CM and CS and investigated the purpose of CM and CS in marriage advice uttered by Ustadz Abdul Somad. This study used a qualitative approach in which narrative analysis technique was applied to analyze the data. The data source was the utterances of Ustadz Abdul Somad (UAS) taken from the recorded video, which was downloaded from YouTube. The data were words, phrases, clauses, and sentences. The data were transcribed by using sonix.ai and reviewed by three language experts (Indonesian, English, and Arabic). As results, this study indicates that Indonesian-Arabic and English-Indonesian CM were found at the word and phrase levels. Then, Arabic-Indonesian CS was found at the inter-sentential and intra-sentential levels. CM and CS employment purposes were various, such as emphasizing the meaning, praising, hoping or praying, translating, and exemplifying.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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 it