Migrant and refugee solidarity in European cities: A meta-synthesis of key themes and approaches
Bibliographic record
Abstract
Across Europe over 700 cities are actively supporting solidarity-based migration policies and practices. Often, these cities are responding to regressive and anti-migrant policies enacted at national and EU levels. In this paper, we examine data collected by Moving Cities on good practices in urban migrant and refugee support through 28 case studies of cities across Europe. Our meta-synthesis of this data highlights key themes across European cities adopting solidarity-based migration policies: the empowerment of municipal actors, collaborative approaches, and integrated frameworks. We also discuss the importance of local context. These findings largely affirm the literature but also contribute a whole-of-government and whole-of-society perspective reflecting an integrated framework to established knowledge on urban solidarity practices and policies. The findings also offer important insights and practical guidance for local policy making. • European cities show patterns in structuring solidarity work, engaging stakeholders, and local policy. • Meta-synthesis of migrant solidarity initiatives across 28 progressive European cities • Three vital municipal actors shape migrant solidarity: mayors, staff, and immigrant councils. • Migrant solidarity relies on partnerships across civil society, regions, and city networks. • European cities adopt integrated approaches to migrant solidarity across society and government.
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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 | 0.049 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.006 |
| 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".