Bibliographic record
Abstract
This paper aims to investigate how the concept of language, culture, identity and difference work together in the implementation of the SA Program for Indonesian students and how important its application for them. Concerning the interrelatedness between the four concepts in the employment of SA program, it is found that there is a chance for Indonesian learners to create new cultures after having interaction and dialogues through English as the target language. They also construct their new identity as people who have high-skills in English, in which they are marked out differently by other people. Furthermore, regarding the importance of joining in the SA program, it is seen that the program is vital to improve students’ language skills, affect their cultural transformation and increase their level of confidence in using English. However, it should be applied carefully and put some considerations due to some challenges, including the limitation of English competence, the different of learning culture characteristics, the likelihood of crisis identity. Hence, all of the emerged insights above might be beneficial for policymakers of the SA program to revisit the regulation and enhance the quality of their guided SA program. Then, it may be useful as well for the SA program educators to design and employ suitable learning strategies to be suitable with students’ demands. Ultimately, it can be useful insights as well for students to be well-prepared before joining the SA program.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".