Welfare Pluralism and a Policy Window in Refugee Policies: The Emergence and Proliferation of Community Sponsorship in Europe, 2013–2023
Bibliographic record
Abstract
Abstract The last decade in Europe has been marked by unprecedented refugee crises. In the face of existing ineffective and insufficient refugee reception and integration systems, and the tension between more unfavourable general attitudes and restrictive refugee policies on the one hand, and calls for more humanitarian and engaged approaches simultaneously articulated in some segments in receiving societies on the other hand, the need for new tools has become even more acute. States and international institutions are looking for new measures and solutions pressured by civil society actors. Among different approaches, those related to community sponsorship (CS) developed in Canada since the 1970s have become particularly important, which reflects an emerging trend towards more welfare pluralism in receiving and supporting refugees. Drawing on the citizen hosting movement, these initiatives utilise and generate civil society engagement and can increase societies’ acceptance of refugees’ admission and support. This paper outlines the development of CS programmes in Europe. Three waves of development of these programmes can be observed following the refugee crisis associated with the Arab Spring in the mid-2010s, the takeover of power by the Taliban in Afghanistan, and the outbreak of the full-scale war in Ukraine after 24 February 2022. We will explain where such programmes are established (and where they are not) and what factors influence this, including the role of policy windows, policy transfer, policy entrepreneurs, social policy models implemented in the countries in question, and political parties. The theoretical underpinning of the study is the multiple streams theory combined with the policy transfer theory and historical institutionalism.
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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 | 0.006 | 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 | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".