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Record W3026891265 · doi:10.1007/978-3-030-34324-8_9

Emerging Reception Economies: A View from Southern Europe

2020· book-chapter· en· W3026891265 on OpenAlexaff
Laura Bartolini, Regina Mantanika, Anna Triandafyllidou

Bibliographic record

VenueIMISCOE research series · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsResidenceEconomyCorporate governanceEconomic geographyGeographyPolitical scienceBusinessEconomicsDemographic economicsFinance

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.939
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.004

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.

Opus teacher head0.092
GPT teacher head0.373
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations7
Published2020
Admission routes1
Has abstractyes

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