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Record W3217510386 · doi:10.1007/978-3-030-81210-2_10

Voluntary and Forced Return Migration Under a Pandemic Crisis

2021· book-chapter· en· W3217510386 on OpenAlexaff
Zeynep Şahin Mencütek

Bibliographic record

VenueIMISCOE research series · 2021
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsToronto Metropolitan University
FundersAlexander von Humboldt-Stiftung
KeywordsPandemicDeportationImmigrationGlobeBusinessPolitical scienceDilemmaCoronavirus disease 2019 (COVID-19)Law

Abstract

fetched live from OpenAlex

Abstract The Covid-19 pandemic has an impact on migrants’ return desires and actual returns across the globe. Border closures in the face of pandemic lead to the panic mobility of those returning home. The ensuing lockdowns and economic difficulties restricted migrant workers’ access to income and protection, pushing them to return. The pandemic brought evident risks for the regular migrants’ access to healthcare, financial security, and social protection, forcing them to consider the return option too. For irregular migrants, the pandemic further increased the risk of forced returns, including detention, deportation, and pushbacks. For all migrants, decisions are marked by a deep dilemma between staying and returning. Meanwhile, receiving, sending, and transit countries, as well as international organisations are involved in return processes by providing logistics, on the one hand, and stigmatising returnees as carriers of virus, on the other. This study is based on desk research and analysis of the scholarly literature, reports, and grey literature from international organizations, civil society reports, scientific blogs, and media reports. An emphasis on returns provides us broader insights to evaluate changing characteristics of migration and mobility in ‘pandemic times’, the governance of returns, its consequences, and the rhetoric about returnees.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.859
Threshold uncertainty score0.999

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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.089
GPT teacher head0.391
Teacher spread0.302 · 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; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations27
Published2021
Admission routes1
Has abstractyes

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