PENGGUNAAN BAHASA BAGI KELOMPOK IMIGRAN DI MAKASSAR: SUATU KAJIAN KONTAK BAHASA
Bibliographic record
Abstract
Language contact is a verbal communication interaction at the same place and time. The phenomenon of language contact can be found in the city of Makassar, both the people of South Sulawesi themselves and immigrant groups from various Middle Eastern countries, where the city of Makassar is used as a temporary area before visiting the destination countries, namely Australia, Canada and America. Seeing the volume of immigrants from 2014 to 2019 living in Makassar, of course every day they interact with the people of Makassar City. The purpose of this article is to describe the use of language for immigrants in interacting with the people of Makassar. The method used is the method of observation and interviews. From the observations it was found that the use of English was only used to interact formally both to UNHCR officers, IOM staff, and to Makassar residents who greeted them in English, Indonesian was also used formally, but in daily interactions a variation of Makassar Malay was used ( BMM) such as when buying, selling, exercising, and other social adaptations. The use of BMM indicates that it is an adaptation effort for language and culture made by immigrant groups to be socially accepted in the Makassar city environment.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 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) | 0.011 | 0.002 |
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".