Multicultural Education in English Language Teaching: A Study of Paradigms, Perceptions, and Implementation Strategies
Bibliographic record
Abstract
This study aims to investigate multicultural education in English language teaching: a study of paradigms, perceptions, and implementation strategies in Indonesia. The mix-method approach was used in this research. Respondents in this study were English teachers and students from three Junior High Schools (SMP) in Indonesia. The quantitative data were collected through Questionnaire, and Qualitative data were collected through interviews. The results showed that teaching English has a positive influence on multicultural education. Multicultural learning implementation strategies have a significant positive effect on English Language Teaching. The paradigm of Multicultural Education has a positive influence on multicultural education. The multicultural education paradigm has a positive influence on the teaching of English. The multicultural learning implementation strategy (x3) has no significant positive effect on multicultural education. Teacher & Student Perception (X2) significantly positively affects English Teaching. Teacher & Student Perception (X2) has no significant positive effect on Multicultural Education. English Teaching can mediate the influence of the Multicultural education Paradigm on Multicultural Education. English Teaching can mediate the influence of Multicultural Learning Implementation Strategy on Multicultural Education. English Teaching can mediate the influence of Teacher & Student Perception on Multicultural 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".