Global Migration and Education: Schools, Children, and Families edited by Leah Adams and Anna Kirova
Bibliographic record
Abstract
Global Migration and Education: Schools, Children, and Families is a collection of 19 studies from 14 countries and 29 authors.It reflects an extraordinary understanding of the complexities that new immigrant and refugee children around the world face in school in their host countries.All the authors present multiple challenges that educators, schools, and school authorities encounter worldwide and offer recommendations about educational policy and practices that can improve education and shared learning experiences for all children.These studies assist educators to see beyond the "notion of problems" and toward the potentially rich learning opportunities that cultural diversity can offer our school systems.Due to the broad scope of the content, the book has relevance for educational researchers, teachers, and policymakers from around the world.Educators who peruse this text will encounter continuity with others seeking new strategies to use with recent immigrants to school systems.The book also has significance to multicultural education, child psychology, comparative and international education, educational foundations, educational policy, and cross-cultural studies.Although each chapter is unique, each reflects a common theme: the adjustments needed when families and children move to a new culture and school.The book is organized around five themes.Each section is prefaced by an introduction that provides insights into the focus of the main theme.Studies in Theme I, Multiple Global Issues for Immigrant Children and the Schools They Attend, investigate the major issues teachers face when immigrant children arrive in their classrooms.The main findings from these chapters focus on the academic underachievement of immigrant students and the fact that past remedial efforts have brought about only small improvements.In particular, detailed in the study from Denmark "Bringing Multicultural Education into the Mainstream: Developing Schools for Minority and Majority Students" is the need for new educational policies that balance academic goals with student-centered approaches for immigrant children.Theme II, They Are Here: Newcomers in the Schools, includes studies conducted in the United States, Israel, and Greece.Outlined are the challenges teachers face in understanding the needs of immigrant and refugee children and their families when they first come to school.The research studies indicate that teachers worldwide are encountering children who are different than themselves, and that these teachers are often not experienced in dealing with newcomers.Relying on stereotypes, they often default to a deficit model, missing out on the possibility of building on a child's many strengths.This is particularly noted in Adams and Shambleau's study from the US: "Teachers',
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".