Code Choices in Marriage Discourse Preach: A Sociolinguistic Analysis
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.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".