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Record W2415827176 · doi:10.1080/1369183x.2016.1192996

Syrian refugees in Turkey: pathways to precarity, differential inclusion, and negotiated citizenship rights

2016· article· en· W2415827176 on OpenAlexafffund
Feyzi Baban, Suzan Ilcan, Kim Rygiel

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsWilfrid Laurier UniversityBalsillie School of International AffairsUniversity of WaterlooTrent University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPrecarityCitizenshipRefugeeInclusion (mineral)Political scienceDifferential (mechanical device)Syrian refugeesGender studiesSociologyCriminologyDemographic economicsSocioeconomicsLawPoliticsEconomics

Abstract

fetched live from OpenAlex

This article addresses the question of how to understand the relation among precarity, differential inclusion, and citizenship status with regard to Syrian refugees in Turkey. Turkey has become host to over 2.7 million Syrian refugees who live in government-run refugee camps and urban centres. Drawing on critical citizenship and migration studies literature, the paper emphasises the Turkish government’s central legal and policy frameworks that provide Syrians with some citizenship rights while simultaneously regulating their status and situating them in a position of limbo. Syrians are not only making claims to citizenship rights but they are also negotiating their access to social services, humanitarian assistance, and employment in different ways. The analysis stresses that Syrian refugees in Turkey continue to be part of the multiple pathways to precarity, differential inclusion, and negotiated citizenship rights.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.009
Scholarly communication0.0050.004
Open science0.0010.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.357
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations378
Published2016
Admission routes2
Has abstractyes

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