Multicultural Education issues, policies, and practices
Bibliographic record
Abstract
Preface, Acknowledgments, Introduction, Multicultural Education: History, Issues, And Practices, Farideh Salili and Rumjahn Hoosain. Cooperative Learning Programs And Multicultural Education: Improving Intergroup Relations, Robert Cooper and Robert E. Slavin. Understanding The Impact Of Disadvantage On Academic Achievement, Colette Van Laar. Academic Adaptation Of Asian Migrant Of Overseas Students In Australia And Canada, Cynthia Leung. Students As Cultural Beings: Motivation, Learning And Achievement Among Students Of Diverse Ethnic Background, Clarence Chi-hung Ng. Multicultural Education In Latvia, Iveta Silova And Guntars Catlaks. Immigration Policy And Multicultural Education In Australia: Charting The Changes, Bob Hill and Rod Allan. Validation Of Multicultural Personality Questionnaire Among An Internationally Oriented Student Population In Taiwan, Stefan T. Mol, Jan-Pieter van Oudenhoven, and Karen I van der Zee. Education Needs For Cross-cultural Convergence In Family Legal Duties And Reciprocal Responsibilities, Oliver C.S. Tzeng. Literature, A Driving Force In Ethnic Identity And Social Responsibility Development, Nancy Hansen-Kerning and Donald T. Mizokawa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".