Emerging Reception Economies: A View from Southern Europe
Bibliographic record
Abstract
Abstract This chapter explores how the recent surge in irregular flows across the Mediterranean has fostered the emergence of new economies of reception that transforms irregular migration from a challenge for border regions to an opportunity or even a strategy for survival. The chapter starts by discussing the available literature on the economies of migration control and the interlinked aspect that can be conceived as the economies of reception. The second section looks at the different types of emergency and longer-term EU funding destined to the reception and processing of migrants arrived by sea and by land in Italy and Greece, and explores the interplay between the different levels of governance and the related challenges that arise. Section three turns to the related emergence of local reception economies, discussing the reception system structures in Greece and Italy, the further impact in local contexts through the employment of reception-related professionals and the ‘refugeeization” of local labour mark, through the (often informal) insertion of migrants with different combinations of residence and work statuses. It is the authors’ contention that, through the channelling of local and regional resources, migrants arrived through irregular channels create a whole set of economic activities, occupations, and professional types and increase or transform the employment of both locals and settled migrants. The chapter is not the outcome of exhaustive empirical research, but map outs the main components of the economies of reception and presents few case studies for Greece and Italy, which are opening a new strand for further research in this field.
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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 | 0.001 | 0.001 |
| 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 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".