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Record W7139137053 · doi:10.64081/mvmnts-3.2-2461

Playing Border Politics with Urban Syrian Refugees

2017· article· W7139137053 on OpenAlexaff
Feyzi Baban, Suzan Ilcan, Kim Rygiel

Bibliographic record

Venuemovements Journal für kritische Migrations- und Grenzregimeforschung · 2017
Typearticle
Language
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsBalsillie School of International AffairsTrent University
Fundersnot available
KeywordsRefugeeSyrian refugeesPoliticsTurkishRefugee crisisInternational relationsPosition (finance)Subject (documents)

Abstract

fetched live from OpenAlex

The attempted military coup in Turkey on July 15, 2016 was also a time of rioting in Ankara’s Önder neighborhood, with many Syrian businesses vandalized. Attacks against Syrians have also occurred periodically in other Turkish cities. With over three million Syrians living in Turkey, such attacks are rare, and yet their occurrences are an example of the insecurities facing Syrians as a result of national, regional, and international border politics. This paper discusses the insecurities facing Syrians in urban centers in Turkey as a consequence of the ambiguous subject position that has been forced upon them as a result of border politics at the national level through Turkey’s temporary protection regime, and solidified at the regional level through the EU-Turkey deal. We argue that such border politics aim to strip Syrian refugees of their political subjectivity and ability to claim rights under the international refugee protection regime by reconstituting Syrians — and indeed the figure of the refugee — as objects of humanitarian assistance rather than political agents with 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)0.003
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 score0.034
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0340.020
Scholarly communication0.0130.005
Open science0.0010.012
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.017
GPT teacher head0.353
Teacher spread0.336 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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