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Record W3188057407 · doi:10.1111/cag.12704

Governance through discipline in the neighbourhood: Syrian refugees and Turkish citizens in urban life

2021· article· en· W3188057407 on OpenAlexfundvenueno aff
Cenk Saraçoğlu, Danièle Bélanger

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRefugeeTurkishContext (archaeology)DisciplinePoliticsPolitical scienceSociologyCorporate governanceCONTESTNeighbourhood (mathematics)GeographyLawBusiness

Abstract

fetched live from OpenAlex

In this paper, we contend that the regulation of refugees by means of discipline is not limited to camps; it also takes place within the context of urban life. This is particularly relevant for Turkey, where only 1.5% of the 3.7 million Syrian refugees live in the camps while the rest are dispersed across Turkish cities. Our fieldwork conducted in the city of İzmir indicates that the processes of governing refugees extend into the neighbourhoods through “informal” disciplinary techniques deployed by citizens. The disciplinary techniques could take various forms, such as socio‐spatial distancing and corporal violence. The discipline in the neighbourhoods sets limits on the Syrians’ collective presence in the city and inculcates the possible results they could face if they attempt to breach these limits. The disciplinary actions of the host community unfold in the context of the Turkish state's legal regulations and discursive strategies that circumscribe Syrians to a specific political position relative to the state and, by extension, to the citizens. The process of disciplining refugees in the neighbourhoods depoliticizes Syrian refugees by obstructing their collective will to contest their precarious status and the exploitative working conditions .

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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 designObservational
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

Citations17
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Geographies / Géographies canadiennesSame topicMigration, Refugees, and IntegrationFrench-language works237,207