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Encounters of Despair: Street-Level Bureaucrat and Migrant Interactions in Sweden and Switzerland

2021· article· en· W3162806470 on OpenAlexvenueno aff
Lisa Marie Borrelli

Bibliographic record

VenueAnthropologica · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsCONTESTNegotiationState (computer science)Power (physics)PoliticsSociologyPolitical economyEnforcementEthnographyPolitical scienceCriminologyLawSocial science

Abstract

fetched live from OpenAlex

Encounters between street-level bureaucrats and the so-called “client of the state” – here the migrant individual with precarious legal status – are characterized by great power imbalances. The dependency relationships that emerge out of public administrative encounters need to be understood as spaces of continuous asymmetrical negotiations. Emotions play a crucial role, not only as a translation of how migrants and bureaucrats mutually shape, contest, and reproduce migration control, but also as a strategic component and a tool for negotiation. Supported by ethnographic data from a Swiss Cantonal Migration Office and a Swedish Border Police Unit, collected between 2016 and 2017, I argue that emotions interweave all migrant-bureaucrat interactions. Their analysis discloses not only the emotional labour of migration enforcement, but also how it is translated into bureaucratically enacted practices, which include physical force, vocal exchanges, documents and spatial means, leading to what Walters (2006) coined “political economies of violence” (438).

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)0.000
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 score0.315
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.062
GPT teacher head0.372
Teacher spread0.310 · 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

Citations20
Published2021
Admission routes1
Has abstractyes

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