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Record W2153949927 · doi:10.18357/mmd11201513521

Migrant Citizenships and Autonomous Mobilities

2015· article· en· W2153949927 on OpenAlexaff
Peter Nyers

Bibliographic record

VenueMigration Mobility & Displacement · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPoliticsCitizenshipAgency (philosophy)DeportationPolitical subjectivityRefugeeNaturalisationSubjectivityFreedom of movementDismissalMigration studiesPolitical scienceSociologyImmigrationPolitical economyGender studiesLawSocial scienceEpistemology

Abstract

fetched live from OpenAlex

The study of the political agency and subjectivity of refugees and migrants has become an increasingly important topic within migration studies. Migration involves struggles around fundamental social and political issues, namely mobility, residence, and citizenship rights. Expressions of this struggle can be found in local actions against detention, deportation, and other border controls; campaigns for regularization and status; the revival of sanctuary cities; and global struggles for freedom of movement. However, the traditional concepts and frameworks of migration do not adequately take into account the full dynamic range of migrant practices of political subject-making. This article analyses the “autonomy of migration” literature within migration studies and critically assesses whether the concepts from this perspective can be mobilized to understand the political agency and subjectivity of migrants. While the autonomist approach to migration makes vital and dynamic contributions to our understanding of migrant political agency, its dismissal of citizenship as an exclusionary concept would benefit from a more nuanced approach.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

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 studies0.0030.014
Scholarly communication0.0040.002
Open science0.0000.005
Research integrity0.0010.001
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 head0.039
GPT teacher head0.319
Teacher spread0.280 · 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

Citations161
Published2015
Admission routes1
Has abstractyes

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