Optimasi Sumber Daya dan Kolaborasi Mulitpihak (Pentaheliks): Suatu Kajian Perencanaan Bahasa
Bibliographic record
Abstract
Indonesia as a multilingual country requires careful language planning so that language can be developed in accordance with the pace of national civilisation. The study in this article is a descriptive study with a qualitative approach. The analysis is based on the data from the literature study. Based on the descriptive results of the literature study, it is found that language planning in Indonesia has fulfilled all elements of language planning starting from planning the status of Indonesian language has the status / position as the national language and state language, languages other than Indonesian and foreign languages are regional languages, and languages in Indonesia other than Indonesian and regional languages are foreign languages. Based on these facts, Indonesia is a country that has a complexity of conditions and language problems. Therefore, optimising resources and collaborating with the pentaheliks model is a must. All elements of policy makers and the community will streamline the language planning.
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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 | 0.001 | 0.001 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".