Educational Policies and Practices of English-Speaking Refugee Resettlement Countries
Bibliographic record
Abstract
Since 2014, the international community has felt overwhelmed by refugees and asylum seekers searching for opportunities in which to rebuild their lives. Indeed, large numbers can result in turmoil and concern in resettlement countries and with national citizens. A climate of fear can result, especially if perpetuated by politicians and media that suggest negative effects resulting from immigration.\nCaught in the crossfire of social and political disagreements about migration are children, most of whom are not included in decisions to leave their homelands. This edited book examines their academic challenges from the perspective of the six English-speaking refugee resettlement countries. Our hope is not only to compare challenges, but also to describe successes by which teachers and policymakers can consider new approaches to help refugee and asylum-seeking children. \nEducational Policies and Practices of English-Speaking Refugee Resettlement Countries offers perspectives from established and new scholars examining educational situations for refugees and asylum seekers. The top three resettlement countries are the United States, Canada, and Australia. For its size, New Zealand is also proportionately a country of high resettlement. New to resettlement are the United Kingdom and the Republic of Ireland. Thus, this collection includes wisdom from countries that began resettlement during World War Two as well as newcomers to the process. In 2018, UNHCR numbers of displaced people reached a record high of 68.5 million. Policymakers, teachers, social service providers, and the general public need to understand ways to help resettled refugees become productive members in their new countries of residence.
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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 | 0.004 | 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 | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".