Similar but Not Same: Language Barriers and the Facet of Life Faced by the Indonesians as International Students in Malaysia
Bibliographic record
Abstract
In the realm of global education, Malaysia stands out as a beacon of excellence, offering a plethora of diverse and flexible academic opportunities.It proudly ranks as the 11th preferred educational destination worldwide, with Indonesia emerging as a pivotal contributor to its international student demography.The annual surge in Indonesian student enrolment in Malaysian universities highlights a trend fueled by the allure of competitive tuition fees, geographical closeness, and shared cultural legacies.However, this growing educational exchange is not without its challenges.Indonesian students in Malaysia navigate a complex linguistic landscape, marked by the country's rich tapestry of languages reflective of its pluralistic society.This research meticulously examines the multifaceted linguistic barriers Indonesian students encounter, both within the academic sphere and in their social interactions.Through an extensive electronic survey, this study delves into the intricacies of academic language comprehension, active participation in academic and extracurricular activities, and the broader spectrum of interpersonal communication, peer acceptance, and adaptation to an English-medium educational setting.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".