Ragam Bahasa Pedagang Kaki Lima di Pasar Pangkalpinang, Bangka Belitung: Kajian Sosiolinguistik
Bibliographic record
Abstract
ABSTRAK Ragam bahasa adalah variasi bahasa yang timbul karena perbedaan konteks sosial, maksud komunikasi, saluran penyampaian, dan latar belakang pembicara. Bahasa lisan para pedagang kaki lima umumnya bersifat tidak formal, mendadak, dan penuh kreativitas. Dengan demikian, mempelajari variasi bahasa yang dipakai oleh pedagang kaki lima bisa memberikan perspektif berharga mengenai identitas sosial dan dinamika komunitas tersebut. Oleh sebab itu, studi ini bertujuan untuk menganalisis variasi bahasa pedagang kaki lima di pasar Pangkalpinang, Bangka Belitung. Penelitian ini adalah suatu analisis sosiolinguistik, karena membahas tentang variasi bahasa. Dengan demikian, penelitian ini dapat memperkaya kosakata bahasa, khususnya bahasa Indonesia. Studi ini adalah penelitian kualitatif menggunakan metode deskriptif kualitatif. Data dalam penelitian ini berasal dari variasi bahasa yang digunakan oleh pedagang kaki lima di pasar Pangkalpinang. Hasil studi menunjukkan bahwa variasi bahasa yang dipakai oleh penjual kepada pembeli adalah dengan bahasa lokal atau bahasa sehari-hari, meskipun kadang-kadang diselingi dengan bahasa daerah. Kata kunci: Ragam bahasa, pedagang kaki lima, sosiolinguistik ABSTRACT Language variety is a variation in language that arises due to differences in social context, communication intent, delivery channel, and speaker background. The spoken language of street vendors is generally informal, abrupt, and full of creativity. Therefore, studying the language variations used by street vendors can provide valuable perspectives on the social identity and dynamics of the community. Therefore, this study aims to analyze the language variations of street vendors in Pangkalpinang market, Bangka Belitung. This research is a sociolinguistic analysis, because it discusses language variations. Thus, this research can enrich the vocabulary of languages, especially Indonesian. This study is a qualitative research using qualitative descriptive methods. The data in this study comes from the language variations used by street vendors in Pangkalpinang market. The results of the study indicate that the language variations used by sellers to buyers are local or everyday languages, although sometimes interspersed with regional languages. Keyword: Variety of languages, street vendors, sociolinguistics
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".