Selective Hospitality: Framing the Reception of Ukrainian Refugees across Three National Contexts
Bibliographic record
Abstract
The EU reception of Ukrainian refugees displaced by the full-scale Russian invasion of 2022 has been lauded as ‘exceptional' in its speed and support, especially as compared to previous migration ‘crises’. This discourse of exceptionality obscures a variety of dynamics, however, including considerable differences between EU Member States, and the key role played by private citizens and initiatives in offering the much lauded ‘hospitality' from which state actors have been largely absent. In this article, we would like to examine critically both the discursive framing as well as the material practices of ‘hospitality' to Ukrainian refugees across three European national contexts by focusing specifically on home-hosting initiatives in the Netherlands, Italy and Poland. In our analysis, we examine both the different scales of hospitality (the ‘where' of reception) as well as different individual motivations for hosting refugees (the ‘why’) across these different contexts, highlighting the importance of both societal orientations as well as wider state-geopolitical positionings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".