FUNGSI RAGAM BAHASA PENJUAL IKAN DI PASAR OKA LAMAWALANG, KECAMATAN LARANTUKA, KABUPATEN FLORES TIMUR
Bibliographic record
Abstract
This article describes the diversity of languages formed by the development of society in various aspects of life which includes various social activities, such as trade, government, health, education, and religion. The variety of languages has functions such as the variety of languages used by fish sellers in Oka market, Lamawalang, Larantuka District, East Flores Regency which grows and develops especially among speakers of the fish seller community. This variety of languages can be found when fish sellers offer their fish to buyers where there is language contact, between Indonesian and local languages. The functions of various languages used by fish sellers in the Oka Lamawalang market, Larantuka District, East Flores Regency with the Oka Lamawalang dialect include (1) instrumental (directive) functions, such as seducing, convincing, and asking, (2) interactional or interaction functions, such as asking , forms of interaction in the form of rejection and forms of agreement interaction, (3) representational or representational (declarative) functions such as showing and expressing, (4) personal functions, such as joy and disappointment.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".