The Inhibition and Communication Approaches of Local Languages Learning Among Millennials
Bibliographic record
Abstract
Local languages which are also referred as mother tongue should be attached to every child as individual. The re-orientation of language due to global influences should not mean forgetting the local language. Globalization and traditions can run simultaneously so that millennial generations are not only proficient in foreign languages, but also understand in using their local languages. This is a communication and culture research. The purpose of this study was to determine the millennials assumptions about local languages and the teaching approaches needed. An integrated teaching approach is needed so that it can restore the millennials’ interest and confidence in speaking their local languages. This research used a descriptive qualitative method with interview techniques, involving millennial generation from Jakarta, West Java and Lampung Provinces. The results of the study show that some of the millennials can speak their local languages but not as active speakers. There are two major obstacles that prevent the millennials to speak their local languages, namely internal and external factors. Internal factor that prevents them from speaking their local languages is family, and the external factors include peers, environment and technology. To encourage the use of local language, the government has issued Regional Regulations (PERDA) so that local languages can be used by daily life such as in schools. In addition, equality communication model can be used in teaching local languages, that include seriousness, openness, acceptance, and flexible teaching This approach is supported by binding local government regulations that require the use of local languages in a variety of contexts, including the language of instruction in education.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".