Bibliographic record
Abstract
Multicultural education is not well known. Rather, it is misunderstood and people have a bias against it. In this paper, in order to challenge ignorance and prejudice against multicultural education, I should examine the perspectives on multicultural education cultivated by Banks, Gay, Grant, Sleeter, and Nieto who are the leading scholars in this field. In fact, there are a variety of theories and practices on this education in countries all around the world such as Britain, Australia, Canada, U.S. Israel, Russia, France, Germany, China, India, Brazil and Japan. Even in U.S. alone there are plenty of other concepts. In addition, not only in the field of education, but also in that of Afrocentlism, bilingual education, Japanese/English as the second language, feminism and so on, there are many mentions on multicultural education. However, in order to keep this discussion manageable, I will limit this discussion to ideas of these five scholars. They all understand multicultural education in sociopolitical context. Banks shows five dimensions on multicultural education which are: 1) content integration, 2) knowledge construction process, 3) prejudice reduction, 4) equity pedagogy and 5) empowering school culture and social structure. Gay sets seven categories of multicultural education which are: 1) developing ethnic and cultural literacy, 2) personal development, 3) attitudes and value clarification, 4) multicultural social competence, 5) basic skill proficiency, 6) educational equity and excellence, and 7) personal empowerment for social reform. Grant and Sleeter develop five approaches as follows: 1) teaching the exceptional and culturally different, 2) human relations, 3) single - group studies, 4) multicultural education, and 5) education that is multicultural and social reconstructionist. Nieto also defines multicultural education in sociopolitical context. She mentions seven definitions which are: 1) antiracist education, 2) basic education, 3) important (education) for all students, 4) pervasive education, 5) education for social justice, 6) a process and 7) critical pedagogy. As a fact, there is a big confusion of the meaning of multicultural education. Multicultural education is very different things to different people. On the other hand, however, as Banks says, "there is general agreement among most scholars and researchers (Banks, 2001, p.3)." The agreement of these five scholars are: 1) education for multicultural literacy, 2) antidiscrimination education, 3) education for social justice which fosters responsible citizens, 4) pervasive education, 5) education for individual empowerment. Multicultural education isn't the therapy for some students but the indispensable idea and perspective in order to create democratic 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".