Bibliographic record
Abstract
This book explores how the rising numbers of refugees entering Europe from 2015 onwards played into fears of cultural, religious, and ethnic differences across the continent. The migrant, or refugee crisis, prompted fierce debate about European norms and values, with some commentators questioning whether mostly Muslim refugees would be able to adhere to these values, and be able to integrate into a predominantly Christian European society. In this volume, philosophers, legal scholars, anthropologists and sociologists, analyze some of these debates and discuss practical strategies to reconcile the values that underpin the European project with multiculturalism and religious pluralism, whilst at the same time safeguarding the rights of refugees to seek asylum.Country case studies in the book are drawn from France, Germany, Greece, Hungary, Italy, the Netherlands, Poland, Spain, Sweden, and the United Kingdom; representing states with long histories of immigration, countries with a more recent refugee arrivals, and countries that want to keep refugees at bay and refuse to admit even the smallest number of asylum seekers. Contributors in the book explore the roles which national and local governments, civil society, and community leaders play in these debates and practices, and ask what strategies are being used to educate refugees about European values, and to facilitate their integration.At a time when debates on refugees and European norms continue to rage, this book provides an important interdisciplinary analysis which will be of interest to European policy makers, and researchers across the fields of migration, law, philosophy, anthropology, sociology, and political science.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".