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Record W2529178362 · doi:10.15173/glj.v7i3.2498

Interns and Infidels: The Transformation of Work and Citizenship in Turkey and the United States under Neo-liberalism

2016· article· en· W2529178362 on OpenAlexvenueno aff
Kaan Ağartan, Cedric de Leon

Bibliographic record

VenueGlobal Labour Journal · 2016
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipLiberalismState (computer science)Political economyConservatismPolitical scienceDeportationPopulationSociologySocial rightsImmigrationLawHuman rightsPolitics

Abstract

fetched live from OpenAlex

<p>How do the dispossessed remain governable under economic insecurity? What explains the persistence of work as a prerequisite to social rights in a time when fewer formal jobs exist? Drawing on a comparison of Turkey and the United States since 1980, we demonstrate that the neo-liberal state deploys different versions of the “work-citizenship nexus” to manage both the shrinking minority who enjoy the benefits of full citizenship and the rest who struggle to attain the rights and privileges of the formally employed. We find that neo-liberal state practices comprise a dual movement. On the one hand, the state in both countries reorients itself toward the market in welfare provision and the regulation of labour relations, capitalising on precarious work structures to bring their populations into the fold of neo-liberal governance. On the other hand, the state directly intervenes in disparate ways to manage those who cannot make it in the market. While the American state uses tactics of mass incarceration and deportation, the Turkish state opts for a blend of social conservatism and authoritarianism. This dual movement of reorientation and direct intervention results in what we call “tiered citizenship regimes” that facilitate the management of the population in each case.</p>

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.314

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.038
GPT teacher head0.348
Teacher spread0.311 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2016
Admission routes1
Has abstractyes

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