The Importance of Multicultural Education in Schools in the Era of ASEAN Economic Community
Bibliographic record
Abstract
The purpose of this paper is to provide an understanding importance of multicultural education for students in the schools relate with diversity in the era of Asean Economic Community. The ASEAN, which groups eleven countries, is a very diverse region, with different dynamics that are owned by their respective countries, particularly when associated with education. Diversities can be seen from the many islands, ethnicity, culture, language and religion. Regardless all those differences, these eleven countries share a similar emphasis on human resource development as a key in developing the whole nation to enter the knowledge-based economy and global environment. Pluralism that is the one of the power and uniqueness in the ASEAN Economic Community that can be interpreted as diversity in unity. Diversity, on the one hand is a blessing, because it actually reflects the diversity of the wealth of cultural treasures. But on the other hand, diversity is also great potential for growing proliferation of conflicts, particularly if such diversity is not able to run well. To build the ASEAN community who recognize and appreciate the differences required processes and better understanding through relevant education. Education, as a fundamental human right, is considered very important and strategic for developing their human resources. The right to education imposes an obligation upon countries to ensure that all children and citizens have opportunities to meet their basic learning needs. Promoting quality and equity education is a common policy for countries regardless their different levels of development. Herein lies the importance of multicultural education. Multicultural education becomes strategic for ASEAN nations to be able to manage the plurality creatively, and can be interpreted as an internalization process of values in educational institutions.
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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 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".